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    ' + + '' + + _("Hide Search Matches") + + "

    " + ) + ); + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords: () => { + document + .querySelectorAll("#searchbox .highlight-link") + .forEach((el) => el.remove()); + document + .querySelectorAll("span.highlighted") + .forEach((el) => el.classList.remove("highlighted")); + localStorage.removeItem("sphinx_highlight_terms") + }, + + initEscapeListener: () => { + // only install a listener if it is really needed + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; + if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { + SphinxHighlight.hideSearchWords(); + event.preventDefault(); + } + }); + }, +}; + +_ready(() => { + /* Do not call highlightSearchWords() when we are on the search page. + * It will highlight words from the *previous* search query. + */ + if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords(); + SphinxHighlight.initEscapeListener(); +}); diff --git a/2.0.0/advanced.html b/2.0.0/advanced.html new file mode 100644 index 000000000..e796cb117 --- /dev/null +++ b/2.0.0/advanced.html @@ -0,0 +1,342 @@ + + + + + + + + Advanced Usage — kraken documentation + + + + + + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +
    +

    Advanced Usage

    +

    Optical character recognition is the serial execution of multiple steps, in the +case of kraken binarization (converting color and grayscale images into bitonal +ones), layout analysis/page segmentation (extracting topological text lines +from an image), recognition (feeding text lines images into an classifiers), +and finally serialization of results into an appropriate format such as hOCR or +ALTO.

    +
    +

    Input Specification

    +

    All kraken subcommands operating on input-output pairs, i.e. producing one +output document for one input document follow the basic syntax:

    +
    $ kraken -i input_1 output_1 -i input_2 output_2 ... subcommand_1 subcommand_2 ... subcommand_n
+
    +
    +

    In particular subcommands may be chained.

    +
    +
    +

    Binarization

    +

    The binarization subcommand accepts almost the same parameters as +ocropus-nlbin. Only options not related to binarization, e.g. skew +detection are missing. In addition, error checking (image sizes, inversion +detection, grayscale enforcement) is always disabled and kraken will happily +binarize any image that is thrown at it.

    +

    Available parameters are:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

    option

    type

    –threshold

    FLOAT

    –zoom

    FLOAT

    –escale

    FLOAT

    –border

    FLOAT

    –perc

    INTEGER RANGE

    –range

    INTEGER

    –low

    INTEGER RANGE

    –high

    INTEGER RANGE

    +
    +
    +

    Page Segmentation and Script Detection

    +

    The segment subcommand access two operations page segmentation into lines and +script detection of those lines.

    +

    Page segmentation is mostly parameterless, although a switch to change the +color of column separators has been retained. The segmentation is written as a +JSON file containing bounding boxes in reading order and +the general text direction (horizontal, i.e. LTR or RTL text in top-to-bottom +reading order or vertical-ltr/rtl for vertical lines read from left-to-right or +right-to-left).

    +

    The script detection splits extracted lines from the segmenter into strip +sharing a particular script that can then be recognized by supplying +appropriate models for each detected script to the ocr subcommand.

    +

    Combined output from both consists of lists in the boxes field corresponding +to a topographical line and containing one or more bounding boxes of a +particular script. Identifiers are ISO 15924 4 character codes.

    +
    $ kraken -i 14.tif lines.txt segment
+$ cat lines.json
+{
+   "boxes" : [
+    [
+        ["Grek", [561, 216, 1626,309]]
+    ],
+    [
+        ["Latn", [2172, 197, 2424, 244]]
+    ],
+    [
+        ["Grek", [1678, 221, 2236, 320]],
+        ["Arab", [2241, 221, 2302, 320]]
+    ],
+
+        ["Grek", [412, 318, 2215, 416]],
+        ["Latn", [2208, 318, 2424, 416]]
+    ],
+    ...
+   ],
+   "text_direction" : "horizontal-tb"
+}
+
    +
    +

    Script detection is automatically enabled; by explicitly disabling script +detection the boxes field will contain only a list of line bounding boxes:

    +
    [546, 216, 1626, 309],
+[2169, 197, 2423, 244],
+[1676, 221, 2293, 320],
+...
+[503, 2641, 848, 2681]
+
    +
    +

    Available page segmentation parameters are:

    + + + + + + + + + + + + + + + + + + + + + + + +

    option

    action

    -d, –text-direction

    Sets principal text direction. Valid values are horizontal-lr, horizontal-rl, vertical-lr, and vertical-rl.

    –scale FLOAT

    Estimate of the average line height on the page

    -m, –maxcolseps

    Maximum number of columns in the input document. Set to 0 for uni-column layouts.

    -b, –black-colseps / -w, –white-colseps

    Switch to black column separators.

    -r, –remove-hlines / -l, –hlines

    Disables prefiltering of small horizontal lines. Improves segmenter output on some Arabic texts.

    +

    The parameters specific to the script identification are:

    + + + + + + + + + + + + + + +

    option

    action

    -s/-n

    Enables/disables script detection

    -a, –allowed-script

    Whitelists specific scripts for detection output. Other detected script runs are merged with their adjacent scripts, after a heuristic pre-merging step.

    +
    +
    +

    Model Repository

    +

    There is a semi-curated repository of freely licensed recognition +models that can be accessed from the command line using a few subcommands. For +evaluating a series of models it is also possible to just clone the repository +using the normal git client.

    +

    The list subcommand retrieves a list of all models available and prints +them including some additional information (identifier, type, and a short +description):

    +
    $ kraken list
+Retrieving model list   ✓
+default (pyrnn) - A converted version of en-default.pyrnn.gz
+toy (clstm) - A toy model trained on 400 lines of the UW3 data set.
+...
+
    +
    +

    To access more detailed information the show subcommand may be used:

    +
    $ kraken show toy
+name: toy.clstm
+
+A toy model trained on 400 lines of the UW3 data set.
+
+author: Benjamin Kiessling (mittagessen@l.unchti.me)
+http://kraken.re
+
    +
    +

    If a suitable model has been decided upon it can be retrieved using the get +subcommand:

    +
    $ kraken get toy
+Retrieving model        ✓
+
    +
    +

    Models will be placed in $XDG_BASE_DIR and can be accessed using their name as +shown by the show command, e.g.:

    +
    $ kraken -i ... ... ocr -m toy
+
    +
    +

    Additions and updates to existing models are always welcome! Just open a pull +request or write an email.

    +
    +
    +

    Recognition

    +

    Recognition requires a grey-scale or binarized image, a page segmentation for +that image, and a model file. In particular there is no requirement to use the +page segmentation algorithm contained in the segment subcommand or the +binarization provided by kraken.

    +

    Multi-script recognition is possible by supplying a script-annotated +segmentation and a mapping between scripts and models:

    +
    $ kraken -i ... ... ocr -m Grek:porson.clstm -m Latn:antiqua.clstm
+
    +
    +

    All polytonic Greek text portions will be recognized using the porson.clstm +model while Latin text will be fed into the antiqua.clstm model. It is +possible to define a fallback model that other text will be fed to:

    +
    $ kraken -i ... ... ocr -m ... -m ... -m default:porson.clstm
+
    +
    +

    It is also possible to disable recognition on a particular script by mapping to +the special model keyword ignore. Ignored lines will still be serialized but +will not contain any recognition results.

    +

    The ocr subcommand is able to serialize the recognition results either as +plain text (default), as hOCR, into ALTO, or abbyyXML containing additional +metadata such as bounding boxes and confidences:

    +
    $ kraken -i ... ... ocr -t # text output
+$ kraken -i ... ... ocr -h # hOCR output
+$ kraken -i ... ... ocr -a # ALTO output
+$ kraken -i ... ... ocr -y # abbyyXML output
+
    +
    +

    hOCR output is slightly different from hOCR files produced by ocropus. Each +ocr_line span contains not only the bounding box of the line but also +character boxes (x_bboxes attribute) indicating the coordinates of each +character. In each line alternating sequences of alphanumeric and +non-alphanumeric (in the unicode sense) characters are put into ocrx_word +spans. Both have bounding boxes as attributes and the recognition confidence +for each character in the x_conf attribute.

    +

    Paragraph detection has been removed as it was deemed to be unduly dependent on +certain typographic features which may not be valid for your input.

    +
    +
    + + +
    + +
    +
    + +
    +
    +
    + ©2015-2024, Benjamin Kiessling. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + + | + Page source +
    + + + + + + \ No newline at end of file diff --git a/2.0.0/api.html b/2.0.0/api.html new file mode 100644 index 000000000..d7927fd4b --- /dev/null +++ b/2.0.0/api.html @@ -0,0 +1,163 @@ + + + + + + + + kraken API — kraken documentation + + + + + + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +
    +

    kraken API

    +

    Kraken provides routines which are usable by third party tools. In general +you can expect function in the kraken package to remain stable. We will try +to keep these backward compatible, but as kraken is still in an early +development stage and the API is still quite rudimentary nothing can be +garantueed.

    +
    +

    kraken.binarization module

    +
    +
    +

    kraken.serialization module

    +
    +
    +

    kraken.pageseg module

    +
    +
    +

    kraken.rpred module

    +
    +
    +

    kraken.transcribe module

    +
    +
    +

    kraken.linegen module

    +
    +
    +

    kraken.lib.models module

    +
    +
    +

    kraken.lib.vgsl module

    +
    +
    +

    kraken.lib.codec

    +
    +
    +

    kraken.lib.train module

    +
    +
    +

    kraken.lib.dataset module

    +
    +
    +

    kraken.lib.ctc_decoder

    +
    +
    + + +
    + +
    +
    + +
    +
    +
    + ©2015-2024, Benjamin Kiessling. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + + | + Page source +
    + + + + + + \ No newline at end of file diff --git a/2.0.0/genindex.html b/2.0.0/genindex.html new file mode 100644 index 000000000..b4ad4c5ee --- /dev/null +++ b/2.0.0/genindex.html @@ -0,0 +1,124 @@ + + + + + + + Index — kraken documentation + + + + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + + +

    Index

    + +
    + K + | M + +
    +

    K

    + + +
      +
    • + kraken + +
      • +
    + +

    M

    + + +
      +
    • + module + +
      • +
    + + + +
    + +
    +
    + +
    +
    +
    + ©2015-2024, Benjamin Kiessling. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + +
    + + + + + + \ No newline at end of file diff --git a/2.0.0/gpu.html b/2.0.0/gpu.html new file mode 100644 index 000000000..f52068e12 --- /dev/null +++ b/2.0.0/gpu.html @@ -0,0 +1,100 @@ + + + + + + + + GPU Acceleration — kraken documentation + + + + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +
    +

    GPU Acceleration

    +

    The latest version of kraken uses a new pytorch backend which enables GPU +acceleration both for training and recognition. Apart from a compatible Nvidia +GPU, CUDA and cuDNN have to be installed so pytorch can run computation on it.

    +
    + + +
    + +
    +
    + +
    +
    +
    + ©2015-2024, Benjamin Kiessling. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + + | + Page source +
    + + + + + + \ No newline at end of file diff --git a/2.0.0/index.html b/2.0.0/index.html new file mode 100644 index 000000000..923620ce4 --- /dev/null +++ b/2.0.0/index.html @@ -0,0 +1,222 @@ + + + + + + + + kraken — kraken documentation + + + + + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +
    +

    kraken

    +
    +
    +

    kraken is a turn-key OCR system forked from ocropus. It is intended to rectify a number of +issues while preserving (mostly) functional equivalence.

    +
    +
    +

    Features

    +

    kraken’s main features are:

    +
    +
    +
    +

    All functionality not pertaining to OCR and prerequisite steps has been +removed, i.e. no more error rate measuring, etc.

    +

    Pull requests and code contributions are always welcome.

    +
    +
    +

    Installation

    +

    kraken requires some external libraries to run. On Debian/Ubuntu they may be +installed using:

    +
    # apt install libpangocairo-1.0 libxml2 libblas3 liblapack3 python3-dev python3-pip
+
    +
    +
    +

    pip

    +
    $ pip3 install kraken
+
    +
    +

    or by running pip in the git repository:

    +
    $ pip3 install .
+
    +
    +
    +
    +

    conda

    +

    If you are running Anaconda/miniconda, use:

    +
    $ conda install -c mittagessen kraken
+
    +
    +
    +
    +

    Models

    +

    Finally you’ll have to scrounge up a recognition model to do the actual +recognition of characters. To download the default English text recognition +model and place it in the user’s kraken directory:

    +
    $ kraken get default
+
    +
    +

    A list of libre models available in the central repository can be retrieved by +running:

    +
    $ kraken list
+
    +
    +

    Model metadata can be extracted using:

    +
    $ kraken show arabic-alam-al-kutub
+name: arabic-alam-al-kutub.clstm
+
+An experimental model for Classical Arabic texts.
+
+Network trained on 889 lines of [0] as a test case for a general Classical
+Arabic model. Ground truth was prepared by Sarah Savant
+<sarah.savant@aku.edu> and Maxim Romanov <maxim.romanov@uni-leipzig.de>.
+
+Vocalization was omitted in the ground truth. Training was stopped at ~35000
+iterations with an accuracy of 97%.
+
+[0] Ibn al-Faqīh (d. 365 AH). Kitāb al-buldān. Edited by Yūsuf al-Hādī, 1st
+edition. Bayrūt: ʿĀlam al-kutub, 1416 AH/1996 CE.
+alphabet:  !()-.0123456789:[] «»،؟ءابةتثجحخدذرزسشصضطظعغفقكلمنهوىي ARABIC
+MADDAH ABOVE, ARABIC HAMZA ABOVE, ARABIC HAMZA BELOW
+
    +
    +
    +
    +
    +

    Quickstart

    +

    Recognizing text on an image using the default parameters including the +prerequisite steps of binarization and page segmentation:

    +
    $ kraken -i image.tif image.txt binarize segment ocr
+Loading RNN     ✓
+Processing      ⣻
+
    +
    +

    To binarize a single image using the nlbin algorithm:

    +
    $ kraken -i image.tif bw.tif binarize
+
    +
    +

    To segment a binarized image into reading-order sorted lines:

    +
    $ kraken -i bw.tif lines.json segment
+
    +
    +

    To OCR a binarized image using the default RNN and the previously generated +page segmentation:

    +
    $ kraken -i bw.tif image.txt ocr --lines lines.json
+
    +
    +

    All commands and their parameters are documented, just add the standard +--help flag for further information.

    +
    +
    +

    Training Tutorial

    +

    There is a training tutorial at Training a kraken model.

    +
    +
    +

    License

    +

    Kraken is provided under the terms and conditions of the Apache 2.0 +License retained +from the original ocropus distribution.

    +
    + + +
    + +
    +
    + +
    +
    +
    + ©2015-2024, Benjamin Kiessling. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + + | + Page source +
    + + + + + + \ No newline at end of file diff --git a/2.0.0/ketos.html b/2.0.0/ketos.html new file mode 100644 index 000000000..5c44fe445 --- /dev/null +++ b/2.0.0/ketos.html @@ -0,0 +1,665 @@ + + + + + + + + Training — kraken documentation + + + + + + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +
    +

    Training

    +

    This page describes the training utilities available through the ketos +command line utility in depth. For a gentle introduction on model training +please refer to the tutorial.

    +

    Thanks to the magic of Connectionist Temporal Classification prerequisites for creating a +new recognition model are quite modest. The basic requirement is a number of +text lines (ground truth) that correspond to line images and some time for +training.

    +
    +

    Transcription

    +

    Transcription is done through local browser based HTML transcription +environments. These are created by the ketos transcribe command line util. +Its basic input is just a number of image files and an output path to write the +HTML file to:

    +
    $ ketos transcribe -o output.html image_1.png image_2.png ...
+
    +
    +

    While it is possible to put multiple images into a single transcription +environment splitting into one-image-per-HTML will ease parallel transcription +by multiple people.

    +

    The above command reads in the image files, converts them to black and white, +tries to split them into line images, and puts an editable text field next to +the image in the HTML. There are a handful of option changing the output:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

    option

    action

    -d, –text-direction

    Sets the principal text direction both for the segmenter and in the HTML. Can be one of horizontal-lr, horizontal-rl, vertical-lr, vertical-rl.

    –scale

    A segmenter parameter giving an estimate of average line height. Usually it shouldn’t be set manually.

    –bw / –orig

    Disables binarization of input images. If color or grayscale training data is desired this option has to be set.

    -m, –maxcolseps

    A segmenter parameter limiting the number of columns that can be found in the input image by setting the maximum number of column separators. Set to 0 to disable column detection.

    -b, –black_colseps / -w, –white_colseps

    A segmenter parameter selecting white or black column separators.

    -f, –font

    The font family to use for rendering the text in the HTML.

    -fs, –font-style

    The font style to use in the HTML.

    -p, –prefill

    A model to use for prefilling the transcription. (Optional)

    -o, –output

    Output HTML file.

    +

    It is possible to use an existing model to prefill the transcription environments:

    +
    $ ketos transcribe -p ~/arabic.mlmodel -p output.html image_1.png image_2.png ...
+
    +
    +

    Transcription has to be diplomatic, i.e. contain the exact character sequence +in the line image, including original orthography. Some deviations, such as +consistently omitting vocalization in Arabic texts, is possible as long as they +are systematic and relatively minor.

    +

    After transcribing a number of lines the results have to be saved, either using +the Download button on the lower right or through the regular Save Page +As function of the browser. All the work done is contained directly in the +saved files and it is possible to save partially transcribed files and continue +work later.

    +

    Next the contents of the filled transcription environments have to be +extracted through the ketos extract command:

    +
    $ ketos extract --output output_directory *.html
+
    +
    +

    There are some options dealing with color images and text normalization:

    + + + + + + + + + + + + + + + + + + + + + + + + + + +

    option

    action

    -b, –binarize / –no-binarize

    Binarizes color/grayscale images (default) or retains the original in the output.

    -u, –normalization

    Normalizes text to one of the following Unicode normalization forms: NFD, NFKD, NFC, NFKC

    -s, –normalize-whitespace / –no-normalize-whitespace

    Normalizes whitespace in extracted text. There are several different Unicode whitespace characters that +are replaced by a standard space when not disabled.

    –reorder / –no-reorder

    Tells ketos to reorder the code +point for each line into +left-to-right order. Unicode +code points are always in +reading order, e.g. the first +code point in an Arabic line +will be the rightmost +character. This option reorders +them into display order, +i.e. the first code point is +the leftmost, the second one +the next from the left and so +on. The train subcommand +does this automatically, so it +usually isn’t needed.

    -r, –rotate / –no-rotate

    Skips rotation of vertical lines.

    -o, –output

    Output directory, defaults to training

    +

    The result will be a directory filled with line image text pairs NNNNNN.png +and NNNNNN.gt.txt and a manifest.txt containing a list of all extracted +lines.

    +
    +
    +

    Training

    +

    The training utility allows training of VGSL specified models +both from scratch and from existing models. Training data is in all cases just +a directory containing image-text file pairs as produced by the +transcribe/extract tools. Here are its command line options:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

    option

    action

    -p, –pad

    Left and right padding around lines

    -o, –output

    Output model file prefix. Defaults to model.

    -s, –spec

    VGSL spec of the network to train. CTC layer +will be added automatically. default: +[1,48,0,1 Cr3,3,32 Do0.1,2 Mp2,2 Cr3,3,64 +Do0.1,2 Mp2,2 S1(1x12)1,3 Lbx100 Do]

    -a, –append

    Removes layers before argument and then +appends spec. Only works when loading an +existing model

    -i, –load

    Load existing file to continue training

    -F, –savefreq

    Model save frequency in epochs during +training

    -R, –report

    Report creation frequency in epochs

    -q, –quit

    Stop condition for training. Set to early +for early stopping (default) or dumb for fixed +number of epochs.

    -N, –epochs

    Number of epochs to train for. Set to -1 for indefinite training.

    –lag

    Number of epochs to wait before stopping +training without improvement. Only used when using early stopping.

    –min-delta

    Minimum improvement between epochs to reset +early stopping. Defaults to 0.005.

    -d, –device

    Select device to use (cpu, cuda:0, cuda:1,…). GPU acceleration requires CUDA.

    –optimizer

    Select optimizer (Adam, SGD, RMSprop).

    -r, –lrate

    Learning rate [default: 0.001]

    -m, –momentum

    Momentum used with SGD optimizer. Ignored otherwise.

    -w, –weight-decay

    Weight decay.

    –schedule

    Sets the learning rate scheduler. May be either constant or 1cycle. For 1cycle +the cycle length is determined by the –epoch option.

    -p, –partition

    Ground truth data partition ratio between train/validation set

    -u, –normalization

    Ground truth Unicode normalization. One of NFC, NFKC, NFD, NFKD.

    -c, –codec

    Load a codec JSON definition (invalid if loading existing model)

    –resize

    Codec/output layer resizing option. If set +to add code points will be added, both +will set the layer to match exactly the +training data, fail will abort if training +data and model codec do not match. Only valid when refining an existing model.

    -n, –reorder / –no-reorder

    Reordering of code points to display order.

    -t, –training-files

    File(s) with additional paths to training data. Used to +enforce an explicit train/validation set split and deal with +training sets with more lines than the command line can process. Can be used more than once.

    -e, –evaluation-files

    File(s) with paths to evaluation data. Overrides the -p parameter.

    –preload / –no-preload

    Hard enable/disable for training data preloading. Preloading +training data into memory is enabled per default for sets with less than 2500 lines.

    –threads

    Number of OpenMP threads when running on CPU. Defaults to min(4, #cores).

    +
    +

    From Scratch

    +

    The absolut minimal example to train a new model is:

    +
    $ ketos train training_data/*.png
+
    +
    +

    Training will continue until the error does not improve anymore and the best +model (among intermediate results) will be saved in the current directory.

    +

    In some cases, such as color inputs, changing the network architecture might be +useful:

    +
    $ ketos train -s '[1,0,0,3 Cr3,3,16 Mp3,3 Lfys64 Lbx128 Lbx256 Do]' syr/*.png
+
    +
    +

    Complete documentation for the network description language can be found on the +VGSL page.

    +

    Sometimes the early stopping default parameters might produce suboptimal +results such as stopping training too soon. Adjusting the minimum delta an/or +lag can be useful:

    +
    $ ketos train --lag 10 --min-delta 0.001 syr/*.png
+
    +
    +

    To switch optimizers from Adam to SGD or RMSprop just set the option:

    +
    $ ketos train --optimizer SGD syr/*.png
+
    +
    +

    It is possible to resume training from a previously saved model:

    +
    $ ketos train -i model_25.mlmodel syr/*.png
+
    +
    +
    +
    +

    Fine Tuning

    +

    Fine tuning an existing model for another typeface or new characters is also +possible with the same syntax as resuming regular training:

    +
    $ ketos train -i model_best.mlmodel syr/*.png
+
    +
    +

    The caveat is that the alphabet of the base model and training data have to be +an exact match. Otherwise an error will be raised:

    +
    $ ketos train -i model_5.mlmodel --no-preload kamil/*.png
+Building training set  [####################################]  100%
+Building validation set  [####################################]  100%
+[0.8616] alphabet mismatch {'~', '»', '8', '9', 'ـ'}
+Network codec not compatible with training set
+[0.8620] Training data and model codec alphabets mismatch: {'ٓ', '؟', '!', 'ص', '،', 'ذ', 'ة', 'ي', 'و', 'ب', 'ز', 'ح', 'غ', '~', 'ف', ')', 'د', 'خ', 'م', '»', 'ع', 'ى', 'ق', 'ش', 'ا', 'ه', 'ك', 'ج', 'ث', '(', 'ت', 'ظ', 'ض', 'ل', 'ط', '؛', 'ر', 'س', 'ن', 'ء', 'ٔ', '«', 'ـ', 'ٕ'}
+
    +
    +

    There are two modes dealing with mismatching alphabets, add and both. +add resizes the output layer and codec of the loaded model to include all +characters in the new training set without removing any characters. both +will make the resulting model an exact match with the new training set by both +removing unused characters from the model and adding new ones.

    +
    $ ketos -v train --resize add -i model_5.mlmodel syr/*.png
+...
+[0.7943] Training set 788 lines, validation set 88 lines, alphabet 50 symbols
+...
+[0.8337] Resizing codec to include 3 new code points
+[0.8374] Resizing last layer in network to 52 outputs
+...
+
    +
    +

    In this example 3 characters were added for a network that is able to +recognize 52 different characters after sufficient additional training.

    +
    $ ketos -v train --resize both -i model_5.mlmodel syr/*.png
+...
+[0.7593] Training set 788 lines, validation set 88 lines, alphabet 49 symbols
+...
+[0.7857] Resizing network or given codec to 49 code sequences
+[0.8344] Deleting 2 output classes from network (46 retained)
+...
+
    +
    +

    In both mode 2 of the original characters were removed and 3 new ones were added.

    +
    +
    +

    Slicing

    +

    Refining on mismatched alphabets has its limits. If the alphabets are highly +different the modification of the final linear layer to add/remove character +will destroy the inference capabilities of the network. In those cases it is +faster to slice off the last few layers of the network and only train those +instead of a complete network from scratch.

    +

    Taking the default network definition as printed in the debug log we can see +the layer indices of the model:

    +
    [0.8760] Creating new model [1,48,0,1 Cr3,3,32 Do0.1,2 Mp2,2 Cr3,3,64 Do0.1,2 Mp2,2 S1(1x12)1,3 Lbx100 Do] with 48 outputs
+[0.8762] layer          type    params
+[0.8790] 0              conv    kernel 3 x 3 filters 32 activation r
+[0.8795] 1              dropout probability 0.1 dims 2
+[0.8797] 2              maxpool kernel 2 x 2 stride 2 x 2
+[0.8802] 3              conv    kernel 3 x 3 filters 64 activation r
+[0.8804] 4              dropout probability 0.1 dims 2
+[0.8806] 5              maxpool kernel 2 x 2 stride 2 x 2
+[0.8813] 6              reshape from 1 1 x 12 to 1/3
+[0.8876] 7              rnn     direction b transposed False summarize False out 100 legacy None
+[0.8878] 8              dropout probability 0.5 dims 1
+[0.8883] 9              linear  augmented False out 48
+
    +
    +

    To remove everything after the initial convolutional stack and add untrained +layers we define a network stub and index for appending:

    +
    $ ketos train -i model_1.mlmodel --append 7 -s '[Lbx256 Do]' syr/*.png
+Building training set  [####################################]  100%
+Building validation set  [####################################]  100%
+[0.8014] alphabet mismatch {'8', '3', '9', '7', '܇', '݀', '݂', '4', ':', '0'}
+Slicing and dicing model ✓
+
    +
    +

    The new model will behave exactly like a new one, except potentially training a +lot faster.

    +
    +
    +
    +

    Testing

    +

    Picking a particular model from a pool or getting a more detailled look on the +recognition accuracy can be done with the test command. It uses transcribed +lines, the test set, in the same format as the train command, recognizes the +line images with one or more models, and creates a detailled report of the +differences from the ground truth for each of them.

    +
    +
    -m, --model
    +

    Model(s) to evaluate.

    +
    +
    -e, --evaluation-files
    +

    File(s) with paths to evaluation data.

    +
    +
    -d, --device
    +

    Select device to use.

    +
    +
    -p, --pad
    +

    Left and right padding around lines.

    +
    +
    +

    Transcriptions are handed to the command in the same way as for the train +command, either through a manifest with -e/–evaluation-files or by just +adding a number of image files as the final argument:

    +
    $ ketos test -m $model -e test.txt test/*.png
+Evaluating $model
+Evaluating  [####################################]  100%
+=== report test_model.mlmodel ===
+
+7012 Characters
+6022 Errors
+14.12%       Accuracy
+
+5226 Insertions
+2    Deletions
+794  Substitutions
+
+Count Missed   %Right
+1567  575    63.31%  Common
+5230  5230   0.00%   Arabic
+215   215    0.00%   Inherited
+
+Errors       Correct-Generated
+773  { ا } - {  }
+536  { ل } - {  }
+328  { و } - {  }
+274  { ي } - {  }
+266  { م } - {  }
+256  { ب } - {  }
+246  { ن } - {  }
+241  { SPACE } - {  }
+207  { ر } - {  }
+199  { ف } - {  }
+192  { ه } - {  }
+174  { ع } - {  }
+172  { ARABIC HAMZA ABOVE } - {  }
+144  { ت } - {  }
+136  { ق } - {  }
+122  { س } - {  }
+108  { ، } - {  }
+106  { د } - {  }
+82   { ك } - {  }
+81   { ح } - {  }
+71   { ج } - {  }
+66   { خ } - {  }
+62   { ة } - {  }
+60   { ص } - {  }
+39   { ، } - { - }
+38   { ش } - {  }
+30   { ا } - { - }
+30   { ن } - { - }
+29   { ى } - {  }
+28   { ذ } - {  }
+27   { ه } - { - }
+27   { ARABIC HAMZA BELOW } - {  }
+25   { ز } - {  }
+23   { ث } - {  }
+22   { غ } - {  }
+20   { م } - { - }
+20   { ي } - { - }
+20   { ) } - {  }
+19   { : } - {  }
+19   { ط } - {  }
+19   { ل } - { - }
+18   { ، } - { . }
+17   { ة } - { - }
+16   { ض } - {  }
+...
+Average accuracy: 14.12%, (stddev: 0.00)
+
    +
    +

    The report(s) contains character accuracy measured per script and a detailled +list of confusions. When evaluating multiple models the last line of the output +will the average accuracy and the standard deviation across all of them.

    +
    +
    +

    Artificial Training Data

    +

    It is possible to rely on artificially created training data, instead of +laborously creating ground truth by manual means. A proper typeface and some +text in the target language will be needed.

    +

    For many popular historical fonts there are free reproductions which quite +closely match printed editions. Most are available in your distribution’s

    +

    repositories and often shipped with TeX Live.

    +

    Some good places to start for non-Latin scripts are:

    +
      +

    • Amiri, a classical Arabic typeface by Khaled +Hosny

      • +

    • The Greek Font Society offers freely +licensed (historical) typefaces for polytonic Greek.

      • +

    • The friendly religious fanatics from SIL +assemble a wide variety of fonts for non-Latin scripts.

      • +
    +

    Next we need some text to generate artificial line images from. It should be a +typical example of the type of printed works you want to recognize and at least +500-1000 lines in length.

    +

    A minimal invocation to the line generation tool will look like this:

    +
    $ ketos linegen -f Amiri da1.txt da2.txt
+Reading texts   ✓
+Read 3692 unique lines
+Σ (len: 99)
+Symbols:  !(),-./0123456789:ABEFGHILMNPRS[]_acdefghiklmnoprstuvyz«»،؟ءآأؤإئابةتثجحخدذرزسشصضطظعغـفقكلمنهوىيپ
+Writing images  ✓
+
    +
    +

    The output will be written to a directory called training_data, although +this may be changed using the -o option. Each text line is rendered using +the Amiri typeface.

    +
    +

    Alphabet and Normalization

    +

    Let’s take a look at important information in the preamble:

    +
    Read 3692 unique lines
+Σ (len: 99)
+Symbols:  !(),-./0123456789:ABEFGHILMNPRS[]_acdefghiklmnoprstuvyz«»،؟ﺀﺁﺃﺅﺈﺋﺎﺑﺔﺘﺜﺠﺤﺧﺩﺫﺭﺰﺴﺸﺼﻀﻄﻈﻌﻐـﻔﻘﻜﻠﻤﻨﻫﻭﻰﻳپ
+
    +
    +

    ketos tells us that it found 3692 unique lines which contained 99 different +symbols or code points. We can see the training data contains all of +the Arabic script including accented precomposed characters, but only a subset +of Latin characters, numerals, and punctuation. A trained model will be able to +recognize only these exact symbols, e.g. a C or j on the page will +never be recognized. Either accept this limitation or add additional text lines +to the training corpus until the alphabet matches your needs.

    +

    We can also force a normalization form using the -u option; per default +none is applied. For example:

    +
    $ ketos linegen -u NFD -f "GFS Philostratos" grc.txt
+Reading texts   ✓
+Read 2860 unique lines
+Σ (len: 132)
+Symbols:  #&'()*,-./0123456789:;ABCDEGHILMNOPQRSTVWXZ]abcdefghiklmnopqrstuvxy §·ΑΒΓΔΕΖΗΘΙΚΛΜΝΞΟΠΡΣΤΥΦΧΨΩαβγδεζηθικλμνξοπρςστυφχψω—‘’“
+Combining Characters: COMBINING GRAVE ACCENT, COMBINING ACUTE ACCENT, COMBINING DIAERESIS, COMBINING COMMA ABOVE, COMBINING REVERSED COMMA ABOVE, COMBINING DOT BELOW, COMBINING GREEK PERISPOMENI, COMBINING GREEK YPOGEGRAMMENI
+
+
+$ ketos linegen -u NFC -f "GFS Philostratos" grc.txt
+Reading texts   ✓
+Read 2860 unique lines
+Σ (len: 231)
+Symbols:  #&'()*,-./0123456789:;ABCDEGHILMNOPQRSTVWXZ]abcdefghiklmnopqrstuvxy §·ΐΑΒΓΔΕΖΘΙΚΛΜΝΞΟΠΡΣΤΦΧΨΩάέήίαβγδεζηθικλμνξοπρςστυφχψωϊϋόύώἀἁἂἃἄἅἈἌἎἐἑἓἔἕἘἙἜἝἠἡἢἣἤἥἦἧἩἭἮἰἱἳἴἵἶἷἸἹἼὀὁὂὃὄὅὈὉὌὐὑὓὔὕὖὗὙὝὠὡὢὤὥὦὧὨὩὰὲὴὶὸὺὼᾄᾐᾑᾔᾗᾠᾤᾧᾳᾶᾷῃῄῆῇῒῖῥῦῬῳῴῶῷ—‘’“
+Combining Characters: COMBINING ACUTE ACCENT, COMBINING DOT BELOW
+
    +
    +

    While there hasn’t been any study on the effect of different normalizations on +recognition accuracy there are some benefits to NFD, namely decreased model +size and easier validation of the alphabet.

    +
    +
    +

    Other Parameters

    +

    Sometimes it is desirable to draw a certain number of lines randomly from one +or more large texts. The -n option does just that:

    +
    $ ketos linegen -u NFD -n 100 -f Amiri da1.txt da2.txt da3.txt da4.txt
+Reading texts   ✓
+Read 114265 unique lines
+Sampling 100 lines      ✓
+Σ (len: 64)
+Symbols:  !(),-./0123456789:[]{}«»،؛؟ءابةتثجحخدذرزسشصضطظعغـفقكلمنهوىي–
+Combining Characters: ARABIC MADDAH ABOVE, ARABIC HAMZA ABOVE, ARABIC HAMZA BELOW
+Writing images ⢿
+
    +
    +

    It is also possible to adjust to amount of degradation/distortion of line +images by using the -s/-r/-d/-ds switches:

    +
    $ ketos linegen -m 0.2 -s 0.002 -r 0.001 -d 3 Downloads/D/A/da1.txt
+Reading texts   ✓
+Read 859 unique lines
+Σ (len: 46)
+Symbols:  !"-.:،؛؟ءآأؤإئابةتثجحخدذرزسشصضطظعغفقكلمنهوىي
+Writing images  ⣽
+
    +
    +

    Sometimes the shaping engine misbehaves using some fonts (notably GFS +Philostratos) by rendering texts in certain normalizations incorrectly if the +font does not contain glyphs for decomposed characters. One sign are misplaced +diacritics and glyphs in different fonts. A workaround is renormalizing the +text for rendering purposes (here to NFC):

    +
    $ ketos linegen -ur NFC -u NFD -f "GFS Philostratos" grc.txt
+
    +
    +
    +
    +
    + + +
    + +
    +
    + +
    +
    +
    + ©2015-2024, Benjamin Kiessling. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + + | + Page source +
    + + + + + + \ No newline at end of file diff --git a/2.0.0/models.html b/2.0.0/models.html new file mode 100644 index 000000000..ec80eeee4 --- /dev/null +++ b/2.0.0/models.html @@ -0,0 +1,155 @@ + + + + + + + + Models — kraken documentation + + + + + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +
    +

    Models

    +

    There are currently three kinds of models containing the recurrent neural +networks doing all the character recognition supported by kraken: pronn +files serializing old pickled pyrnn models as protobuf, clstm’s native +serialization, and versatile Core ML models.

    +
    +

    pyrnn

    +

    These are serialized instances of python lstm.SeqRecognizer objects. Using +such a model just entails loading the pickle and calling the appropriate +functions to perform recognition much like a shared library in other +programming languages.

    +

    Support for these models has been dropped with kraken 1.0 as python 2.7 is +phased out.

    +
    +
    +

    pronn

    +

    Legacy python models can be converted to a protobuf based serialization. These +are loadable by kraken 1.0 and will be automatically converted to Core ML.

    +

    Protobuf models have several advantages over pickled ones. They are noticeably +smaller (80Mb vs 1.8Mb for the default model), don’t allow arbitrary code +execution, and are upward compatible with python 3. Because they are so much +more lightweight they are also loaded much faster.

    +
    +
    +

    clstm

    +

    clstm, a small and fast implementation of +LSTM networks that was used in previous kraken versions. The model files can be +loaded with pytorch-based kraken and will be converted to Core ML.

    +
    +
    +

    CoreML

    +

    Core ML allows arbitrary network architectures in a compact serialization with +metadata. This is the default format in pytorch-based kraken.

    +
    +
    +

    Conversion

    +

    Per default pronn/clstm models are automatically converted to the new Core ML +format when explicitely defined using the -m option to the ocr utility +on the command line. They are stored in the user kraken directory (default is +~/.kraken) and will be automatically substituted in future runs.

    +

    If conversion is not desired, e.g. because there is a bug in the conversion +routine, it can be disabled using the --disable-autoconversion switch.

    +
    +
    + + +
    + +
    +
    + +
    +
    +
    + ©2015-2024, Benjamin Kiessling. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + + | + Page source +
    + + + + + + \ No newline at end of file diff --git a/2.0.0/objects.inv b/2.0.0/objects.inv new file mode 100644 index 000000000..35f9fb65d --- /dev/null +++ b/2.0.0/objects.inv @@ -0,0 +1,7 @@ +# Sphinx inventory version 2 +# Project: kraken +# Version: +# The remainder of this file is compressed using zlib. +xڅ�1o� �w~�I��]�y�"���$�)\md�-C����l q�np���Nu�k4���k#o�x�^*w��d[��wnJ�N�d# {��6����S�柨W��v%jU0 5 �V�QF�O�;W ? GV����_��/.� �;�Tc��M�p/�o�Kc�]ccd,r��b�P+��b�.�=��s�N��B���9��[�y��c�m�͍`;�Zy��GV��j̣�3��d��;Q� ���D�b�Ln�`����>����y�2�$������8s�S���t���j�- +��}� +qA棞i'M� \ No newline at end of file diff --git a/2.0.0/py-modindex.html b/2.0.0/py-modindex.html new file mode 100644 index 000000000..63ccc7523 --- /dev/null +++ b/2.0.0/py-modindex.html @@ -0,0 +1,114 @@ + + + + + + + Python Module Index — kraken documentation + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + + +

    Python Module Index

    + +
    + k +
    + + + + + + + +
     
    + k
    + kraken +
    + + +
    + +
    +
    + +
    +
    +
    + ©2015-2024, Benjamin Kiessling. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + +
    + + + + + + \ No newline at end of file diff --git a/2.0.0/search.html b/2.0.0/search.html new file mode 100644 index 000000000..5123910d4 --- /dev/null +++ b/2.0.0/search.html @@ -0,0 +1,113 @@ + + + + + + + Search — kraken documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +

    Search

    + + + + +

    + Searching for multiple words only shows matches that contain + all words. +

    + + +
    + + + +
    + + +
    + + +
    + +
    +
    + +
    +
    +
    + ©2015-2024, Benjamin Kiessling. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + +
    + + + + + + \ No newline at end of file diff --git a/2.0.0/searchindex.js b/2.0.0/searchindex.js new file mode 100644 index 000000000..e112d199e --- /dev/null +++ b/2.0.0/searchindex.js @@ -0,0 +1 @@ +Search.setIndex({"alltitles": {"Advanced Usage": [[0, "advanced-usage"]], "Alphabet and Normalization": [[4, "alphabet-and-normalization"]], "Artificial Training Data": [[4, "artificial-training-data"]], "Basics": [[7, "basics"]], "Binarization": [[0, "binarization"]], "Conversion": [[5, "conversion"]], "Convolutional Layers": [[7, "convolutional-layers"]], "CoreML": [[5, "coreml"]], "Evaluation and Validation": [[6, "evaluation-and-validation"]], "Examples": [[7, "examples"]], "Features": [[3, "features"]], "Fine Tuning": [[4, "fine-tuning"]], "From Scratch": [[4, "from-scratch"]], "GPU Acceleration": [[2, "gpu-acceleration"]], "Helper and Plumbing Layers": [[7, "helper-and-plumbing-layers"]], "Image acquisition and preprocessing": [[6, "image-acquisition-and-preprocessing"]], "Input Specification": [[0, "input-specification"]], "Installation": [[3, "installation"]], "Installing kraken": [[6, "installing-kraken"]], "License": [[3, "license"]], "Max Pool": [[7, "max-pool"]], "Model Repository": [[0, "model-repository"]], "Models": [[3, "models"], [5, "models"]], "Other Parameters": [[4, "other-parameters"]], "Page Segmentation and Script Detection": [[0, "page-segmentation-and-script-detection"]], "Quickstart": [[3, "quickstart"]], "Recognition": [[0, "recognition"], [6, "recognition"]], "Recurrent Layers": [[7, "recurrent-layers"]], "Regularization Layers": [[7, "regularization-layers"]], "Reshape": [[7, "reshape"]], "Slicing": [[4, "slicing"]], "Testing": [[4, "testing"]], "Training": [[4, "training"], [4, "id1"], [6, "id1"]], "Training Tutorial": [[3, "training-tutorial"]], "Training a kraken model": [[6, "training-a-kraken-model"]], "Transcription": [[4, "transcription"], [6, "transcription"]], "VGSL network specification": [[7, "vgsl-network-specification"]], "clstm": [[5, "clstm"]], "conda": [[3, "conda"]], "kraken": [[3, "kraken"]], "kraken API": [[1, "module-kraken"]], "kraken.binarization module": [[1, "kraken-binarization-module"]], "kraken.lib.codec": [[1, "kraken-lib-codec"]], "kraken.lib.ctc_decoder": [[1, "kraken-lib-ctc-decoder"]], "kraken.lib.dataset module": [[1, "kraken-lib-dataset-module"]], "kraken.lib.models module": [[1, "kraken-lib-models-module"]], "kraken.lib.train module": [[1, "kraken-lib-train-module"]], "kraken.lib.vgsl module": [[1, "kraken-lib-vgsl-module"]], "kraken.linegen module": [[1, "kraken-linegen-module"]], "kraken.pageseg module": [[1, "kraken-pageseg-module"]], "kraken.rpred module": [[1, "kraken-rpred-module"]], "kraken.serialization module": [[1, "kraken-serialization-module"]], "kraken.transcribe module": [[1, "kraken-transcribe-module"]], "pip": [[3, "pip"]], "pronn": [[5, "pronn"]], "pyrnn": [[5, "pyrnn"]]}, "docnames": ["advanced", "api", "gpu", "index", "ketos", "models", "training", "vgsl"], 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    +
    +
    + + +
    + +
    +

    Training a kraken model

    +

    kraken is an optical character recognition package that can be trained fairly +easily for a large number of scripts. In contrast to other system requiring +segmentation down to glyph level before classification, it is uniquely suited +for the recognition of connected scripts, because the neural network is trained +to assign correct character to unsegmented training data.

    +

    Training a new model for kraken requires a variable amount of training data +manually generated from page images which have to be typographically similar to +the target prints that are to be recognized. As the system works on unsegmented +inputs for both training and recognition and its base unit is a text line, +training data are just transcriptions aligned to line images.

    +
    +

    Installing kraken

    +

    The easiest way to install and use kraken is through conda. kraken works both on Linux and Mac OS +X. After installing conda, download the environment file and create the +environment for kraken:

    +
    $ wget https://raw.githubusercontent.com/mittagessen/kraken/master/environment.yml
+$ conda env create -f environment.yml
+
    +
    +

    Each time you want to use the kraken environment in a shell is has to be +activated first:

    +
    $ conda activate kraken
+
    +
    +
    +
    +

    Image acquisition and preprocessing

    +

    First a number of high quality scans, preferably color or grayscale and at +least 300dpi are required. Scans should be in a lossless image format such as +TIFF or PNG, images in PDF files have to be extracted beforehand using a tool +such as pdftocairo or pdfimages. While each of these requirements can +be relaxed to a degree, the final accuracy will suffer to some extent. For +example, only slightly compressed JPEG scans are generally suitable for +training and recognition.

    +

    Depending on the source of the scans some preprocessing such as splitting scans +into pages, correcting skew and warp, and removing speckles is usually +required. For complex layouts such as newspapers it is advisable to split the +page manually into columns as the line extraction algorithm run to create +transcription environments does not deal well with non-codex page layouts. A +fairly user-friendly software for semi-automatic batch processing of image +scans is Scantailor albeit most work can be done +using a standard image editor.

    +

    The total number of scans required depends on the nature of the script to be +recognized. Only features that are found on the page images and training data +derived from it can later be recognized, so it is important that the coverage +of typographic features is exhaustive. Training a single script model for a +fairly small script such as Arabic or Hebrew requires at least 800 lines, while +multi-script models, e.g. combined polytonic Greek and Latin, will require +significantly more transcriptions.

    +

    There is no hard rule for the amount of training data and it may be required to +retrain a model after the initial training data proves insufficient. Most +western texts contain between 25 and 40 lines per page, therefore upward of +30 pages have to be preprocessed and later transcribed.

    +
    +
    +

    Transcription

    +

    Transcription is done through local browser based HTML transcription +environments. These are created by the ketos transcribe command line util +that is part of kraken. Its basic input is just a number of image files and an +output path to write the HTML file to:

    +
    $ ketos transcribe -o output.html image_1.png image_2.png ...
+
    +
    +

    While it is possible to put multiple images into a single transcription +environment splitting into one-image-per-HTML will ease parallel transcription +by multiple people.

    +

    The above command reads in the image files, converts them to black and white if +necessary, tries to split them into line images, and puts an editable text +field next to the image in the HTML.

    +

    Transcription has to be diplomatic, i.e. contain the exact character sequence +in the line image, including original orthography. Some deviations, such as +consistently omitting vocalization in Arabic texts, is possible as long as they +are systematic and relatively minor.

    +
    +

    Note

    +

    The page segmentation algorithm extracting lines from images is +optimized for western page layouts and may recognize lines +erroneously, lumping multiple lines together or cutting them in half. +The most efficient way to deal with these errors is just skipping the +affected lines by leaving the text box empty.

    +
    +
    +

    Tip

    +

    Copy-paste transcription can significantly speed up the whole process. +Either transcribe scans of a work where a digital edition already +exists (but does not for typographically similar prints) or find a +sufficiently similar edition as a base.

    +
    +

    After transcribing a number of lines the results have to be saved, either using +the Download button on the lower left or through the regular Save Page +As (CTRL+S) function of the browser. All the work done is contained directly +in the saved files and it is possible to save partially transcribed files and +continue work later.

    +

    Next the contents of the filled transcription environments have to be +extracted through the ketos extract command:

    +
    $ ketos extract --output output_directory --normalization NFD *.html
+
    +
    +

    with

    +
    +
    --output
    +

    The output directory where all line image-text pairs (training data) +are written, defaulting to training/

    +
    +
    --normalization
    +

    Unicode has code points to encode most glyphs encountered in the wild. +A lesser known feature is that there usually are multiple ways to +encode a glyph. Unicode normalization ensures that equal glyphs are +encoded in the same way, i.e. that the encoded representation across +the training data set is consistent and there is only one way the +network can recognize a particular feature on the page. Usually it is +sufficient to set the normalization to Normalization Form +Decomposed (NFD), as it reduces the the size of the overall script to +be recognized slightly.

    +
    +
    +

    The result will be a directory filled with line image text pairs NNNNNN.png +and NNNNNN.gt.txt and a manifest.txt containing a list of all extracted +lines.

    +
    +

    Note

    +

    At this point it is recommended to review the content of the training +data directory before proceeding.

    +
    +
    +
    +

    Training

    +

    The training data in output_dir may now be used to train a new model by +invoking the ketos train command. Just hand a list of images to the command +such as:

    +
    $ ketos train output_dir/*.png
+
    +
    +

    to start training.

    +

    A number of lines will be split off into a separate held-out set that is used +to estimate the actual recognition accuracy achieved in the real world. These +are never shown to the network during training but will be recognized +periodically to evaluate the accuracy of the model. Per default the validation +set will comprise of 10% of the training data.

    +

    Basic model training is mostly automatic albeit there are multiple parameters +that can be adjusted:

    +
    +
    --output
    +

    Sets the prefix for models generated during training. They will best as +prefix_epochs.mlmodel.

    +
    +
    --report
    +

    How often evaluation passes are run on the validation set. It is an +integer equal or larger than 1 with 1 meaning a report is created each +time the complete training set has been seen by the network.

    +
    +
    --savefreq
    +

    How often intermediate models are saved to disk. It is an integer with +the same semantics as --report.

    +
    +
    --load
    +

    Continuing training is possible by loading an existing model file with +--load. To continue training from a base model with another +training set refer to the full ketos documentation.

    +
    +
    --preload
    +

    Enables/disables preloading of the training set into memory for +accelerated training. The default setting preloads data sets with less +than 2500 lines, explicitly adding --preload will preload arbitrary +sized sets. --no-preload disables preloading in all circumstances.

    +
    +
    +

    Training a network will take some time on a modern computer, even with the +default parameters. While the exact time required is unpredictable as training +is a somewhat random process a rough guide is that accuracy seldomly improves +after 50 epochs reached between 8 and 24 hours of training.

    +

    When to stop training is a matter of experience; the default setting employs a +fairly reliable approach known as early stopping that stops training as soon as +the error rate on the validation set doesn’t improve anymore. This will +prevent overfitting, i.e. +fitting the model to recognize only the training data properly instead of the +general patterns contained therein.

    +
    $ ketos train output_dir/*.png
+Building training set  [####################################]  100%
+Building validation set  [####################################]  100%
+[270.2364] alphabet mismatch {'9', '8', '݂', '3', '݀', '4', '1', '7', '5', '\xa0'}
+Initializing model ✓
+Accuracy report (0) -1.5951 3680 9550
+epoch 0/-1  [####################################]  788/788
+Accuracy report (1) 0.0245 3504 3418
+epoch 1/-1  [####################################]  788/788
+Accuracy report (2) 0.8445 3504 545
+epoch 2/-1  [####################################]  788/788
+Accuracy report (3) 0.9541 3504 161
+epoch 3/-1  [------------------------------------]  13/788  0d 00:22:09
+...
+
    +
    +

    By now there should be a couple of models model_name-1.mlmodel, +model_name-2.mlmodel, … in the directory the script was executed in. Lets +take a look at each part of the output.

    +
    Building training set  [####################################]  100%
+Building validation set  [####################################]  100%
+
    +
    +

    shows the progress of loading the training and validation set into memory. This +might take a while as preprocessing the whole set and putting it into memory is +computationally intensive. Loading can be made faster without preloading at the +cost of performing preprocessing repeatedlyduring the training process.

    +
    [270.2364] alphabet mismatch {'9', '8', '݂', '3', '݀', '4', '1', '7', '5', '\xa0'}
+
    +
    +

    is a warning about missing characters in either the validation or training set, +i.e. that the alphabets of the sets are not equal. Increasing the size of the +validation set will often remedy this warning.

    +
    Accuracy report (2) 0.8445 3504 545
+
    +
    +

    this line shows the results of the validation set evaluation. The error after 2 +epochs is 545 incorrect characters out of 3504 characters in the validation set +for a character accuracy of 84.4%. It should decrease fairly rapidly. If +accuracy remains around 0.30 something is amiss, e.g. non-reordered +right-to-left or wildly incorrect transcriptions. Abort training, correct the +error(s) and start again.

    +

    After training is finished the best model is saved as +model_name_best.mlmodel. It is highly recommended to also archive the +training log and data for later reference.

    +

    ketos can also produce more verbose output with training set and network +information by appending one or more -v to the command:

    +
    $ ketos -vv train syr/*.png
+[0.7272] Building ground truth set from 876 line images
+[0.7281] Taking 88 lines from training for evaluation
+...
+[0.8479] Training set 788 lines, validation set 88 lines, alphabet 48 symbols
+[0.8481] alphabet mismatch {'\xa0', '0', ':', '݀', '܇', '݂', '5'}
+[0.8482] grapheme       count
+[0.8484] SPACE  5258
+[0.8484]        ܐ       3519
+[0.8485]        ܘ       2334
+[0.8486]        ܝ       2096
+[0.8487]        ܠ       1754
+[0.8487]        ܢ       1724
+[0.8488]        ܕ       1697
+[0.8489]        ܗ       1681
+[0.8489]        ܡ       1623
+[0.8490]        ܪ       1359
+[0.8491]        ܬ       1339
+[0.8491]        ܒ       1184
+[0.8492]        ܥ       824
+[0.8492]        .       811
+[0.8493] COMBINING DOT BELOW    646
+[0.8493]        ܟ       599
+[0.8494]        ܫ       577
+[0.8495] COMBINING DIAERESIS    488
+[0.8495]        ܚ       431
+[0.8496]        ܦ       428
+[0.8496]        ܩ       307
+[0.8497] COMBINING DOT ABOVE    259
+[0.8497]        ܣ       256
+[0.8498]        ܛ       204
+[0.8498]        ܓ       176
+[0.8499]        ܀       132
+[0.8499]        ܙ       81
+[0.8500]        *       66
+[0.8501]        ܨ       59
+[0.8501]        ܆       40
+[0.8502]        [       40
+[0.8503]        ]       40
+[0.8503]        1       18
+[0.8504]        2       11
+[0.8504]        ܇       9
+[0.8505]        3       8
+[0.8505]                6
+[0.8506]        5       5
+[0.8506] NO-BREAK SPACE 4
+[0.8507]        0       4
+[0.8507]        6       4
+[0.8508]        :       4
+[0.8508]        8       4
+[0.8509]        9       3
+[0.8510]        7       3
+[0.8510]        4       3
+[0.8511] SYRIAC FEMININE DOT    1
+[0.8511] SYRIAC RUKKAKHA        1
+[0.8512] Encoding training set
+[0.9315] Creating new model [1,1,0,48 Lbx100 Do] with 49 outputs
+[0.9318] layer          type    params
+[0.9350] 0              rnn     direction b transposed False summarize False out 100 legacy None
+[0.9361] 1              dropout probability 0.5 dims 1
+[0.9381] 2              linear  augmented False out 49
+[0.9918] Constructing RMSprop optimizer (lr: 0.001, momentum: 0.9)
+[0.9920] Set OpenMP threads to 4
+[0.9920] Moving model to device cpu
+[0.9924] Starting evaluation run
+
    +
    +

    indicates that the training is running on 788 transcribed lines and a +validation set of 88 lines. 49 different classes, i.e. Unicode code points, +where found in these 788 lines. These affect the output size of the network; +obviously only these 49 different classes/code points can later be output by +the network. Importantly, we can see that certain characters occur markedly +less often than others. Characters like the Syriac feminine dot and numerals +that occur less than 10 times will most likely not be recognized well by the +trained net.

    +
    +
    +

    Evaluation and Validation

    +

    While output during training is detailed enough to know when to stop training +one usually wants to know the specific kinds of errors to expect. Doing more +in-depth error analysis also allows to pinpoint weaknesses in the training +data, e.g. above average error rates for numerals indicate either a lack of +representation of numerals in the training data or erroneous transcription in +the first place.

    +

    First the trained model has to be applied to some line transcriptions with the +ketos test command:

    +
    $ ketos test -m syriac_best.mlmodel lines/*.png
+Loading model syriac_best.mlmodel ✓
+Evaluating syriac_best.mlmodel
+Evaluating  [#-----------------------------------]    3%  00:04:56
+...
+
    +
    +

    After all lines have been processed a evaluation report will be printed:

    +
    === report  ===
+
+35619     Characters
+336       Errors
+99.06%    Accuracy
+
+157       Insertions
+81        Deletions
+98        Substitutions
+
+Count     Missed  %Right
+27046     143     99.47%  Syriac
+7015      52      99.26%  Common
+1558      60      96.15%  Inherited
+
+Errors    Correct-Generated
+25        {  } - { COMBINING DOT BELOW }
+25        { COMBINING DOT BELOW } - {  }
+15        { . } - {  }
+15        { COMBINING DIAERESIS } - {  }
+12        { ܢ } - {  }
+10        {  } - { . }
+8 { COMBINING DOT ABOVE } - {  }
+8 { ܝ } - {  }
+7 { ZERO WIDTH NO-BREAK SPACE } - {  }
+7 { ܆ } - {  }
+7 { SPACE } - {  }
+7 { ܣ } - {  }
+6 {  } - { ܝ }
+6 { COMBINING DOT ABOVE } - { COMBINING DIAERESIS }
+5 { ܙ } - {  }
+5 { ܬ } - {  }
+5 {  } - { ܢ }
+4 { NO-BREAK SPACE } - {  }
+4 { COMBINING DIAERESIS } - { COMBINING DOT ABOVE }
+4 {  } - { ܒ }
+4 {  } - { COMBINING DIAERESIS }
+4 { ܗ } - {  }
+4 {  } - { ܬ }
+4 {  } - { ܘ }
+4 { ܕ } - { ܢ }
+3 {  } - { ܕ }
+3 { ܐ } - {  }
+3 { ܗ } - { ܐ }
+3 { ܝ } - { ܢ }
+3 { ܀ } - { . }
+3 {  } - { ܗ }
+
+  .....
+
    +
    +

    The first section of the report consists of a simple accounting of the number +of characters in the ground truth, the errors in the recognition output and the +resulting accuracy in per cent.

    +

    The next table lists the number of insertions (characters occuring in the +ground truth but not in the recognition output), substitutions (misrecognized +characters), and deletions (superfluous characters recognized by the model).

    +

    Next is a grouping of errors (insertions and substitutions) by Unicode script.

    +

    The final part of the report are errors sorted by frequency and a per +character accuracy report. Importantly most errors are incorrect recognition of +combining marks such as dots and diaereses. These may have several sources: +different dot placement in training and validation set, incorrect transcription +such as non-systematic transcription, or unclean speckled scans. Depending on +the error source, correction most often involves adding more training data and +fixing transcriptions. Sometimes it may even be advisable to remove +unrepresentative data from the training set.

    +
    +
    +

    Recognition

    +

    The kraken utility is employed for all non-training related tasks. Optical +character recognition is a multi-step process consisting of binarization +(conversion of input images to black and white), page segmentation (extracting +lines from the image), and recognition (converting line image to character +sequences). All of these may be run in a single call like this:

    +
    $ kraken -i INPUT_IMAGE OUTPUT_FILE binarize segment ocr -m MODEL_FILE
+
    +
    +

    producing a text file from the input image. There are also hocr and ALTO output +formats available through the appropriate switches:

    +
    $ kraken -i ... ocr -h
+$ kraken -i ... ocr -a
+
    +
    +

    For debugging purposes it is sometimes helpful to run each step manually and +inspect intermediate results:

    +
    $ kraken -i INPUT_IMAGE BW_IMAGE binarize
+$ kraken -i BW_IMAGE LINES segment
+$ kraken -i BW_IMAGE OUTPUT_FILE ocr -l LINES ...
+
    +
    +

    It is also possible to recognize more than one file at a time by just chaining +-i ... ... clauses like this:

    +
    $ kraken -i input_1 output_1 -i input_2 output_2 ...
+
    +
    +

    Finally, there is an central repository containing freely available models. +Getting a list of all available models:

    +
    $ kraken list
+
    +
    +

    Retrieving model metadata for a particular model:

    +
    $ kraken show arabic-alam-al-kutub
+name: arabic-alam-al-kutub.mlmodel
+
+An experimental model for Classical Arabic texts.
+
+Network trained on 889 lines of [0] as a test case for a general Classical
+Arabic model. Ground truth was prepared by Sarah Savant
+<sarah.savant@aku.edu> and Maxim Romanov <maxim.romanov@uni-leipzig.de>.
+
+Vocalization was omitted in the ground truth. Training was stopped at ~35000
+iterations with an accuracy of 97%.
+
+[0] Ibn al-Faqīh (d. 365 AH). Kitāb al-buldān. Edited by Yūsuf al-Hādī, 1st
+edition. Bayrūt: ʿĀlam al-kutub, 1416 AH/1996 CE.
+alphabet:  !()-.0123456789:[] «»،؟ءابةتثجحخدذرزسشصضطظعغفقكلمنهوىي ARABIC
+MADDAH ABOVE, ARABIC HAMZA ABOVE, ARABIC HAMZA BELOW
+
    +
    +

    and actually fetching the model:

    +
    $ kraken get arabic-alam-al-kutub
+
    +
    +

    The downloaded model can then be used for recognition by the name shown in its metadata, e.g.:

    +
    $ kraken -i INPUT_IMAGE OUTPUT_FILE binarize segment ocr -m arabic-alam-al-kutub.mlmodel
+
    +
    +

    For more documentation see the kraken website.

    +
    +
    + + +
    + +
    +
    + +
    +
    +
    + ©2015-2024, Benjamin Kiessling. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + + | + Page source +
    + + + + + + \ No newline at end of file diff --git a/2.0.0/vgsl.html b/2.0.0/vgsl.html new file mode 100644 index 000000000..1259c9378 --- /dev/null +++ b/2.0.0/vgsl.html @@ -0,0 +1,272 @@ + + + + + + + + VGSL network specification — kraken documentation + + + + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +
    +

    VGSL network specification

    +

    kraken implements a dialect of the Variable-size Graph Specification Language +(VGSL), enabling the specification of different network architectures for image +processing purposes using a short definition string.

    +
    +

    Basics

    +

    A VGSL specification consists of an input block, one or more layers, and an +output block. For example:

    +
    [1,48,0,1 Cr3,3,32 Mp2,2 Cr3,3,64 Mp2,2 S1(1x12)1,3 Lbx100 Do O1c103]
+
    +
    +

    The first block defines the input in order of [batch, heigh, width, channels] +with zero-valued dimensions being variable. Integer valued height or width +input specifications will result in the input images being automatically scaled +in either dimension.

    +

    When channels are set to 1 grayscale or B/W inputs are expected, 3 expects RGB +color images. Higher values in combination with a height of 1 result in the +network being fed 1 pixel wide grayscale strips scaled to the size of the +channel dimension.

    +

    After the input, a number of layers are defined. Layers operate on the channel +dimension; this is intuitive for convolutional layers but a recurrent layer +doing sequence classification along the width axis on an image of a particular +height requires the height dimension to be moved to the channel dimension, +e.g.:

    +
    [1,48,0,1 S1(1x48)1,3 Lbx100 O1c103]
+
    +
    +

    or using the alternative slightly faster formulation:

    +
    [1,1,0,48 Lbx100 O1c103]
+
    +
    +

    Finally an output definition is appended. When training sequence classification +networks with the provided tools the appropriate output definition is +automatically appended to the network based on the alphabet of the training +data.

    +
    +
    +

    Examples

    +
    [1,1,0,48 Lbx100 Do 01c59]
+
+Creating new model [1,1,0,48 Lbx100 Do] with 59 outputs
+layer           type    params
+0               rnn     direction b transposed False summarize False out 100 legacy None
+1               dropout probability 0.5 dims 1
+2               linear  augmented False out 59
+
    +
    +

    A simple recurrent recognition model with a single LSTM layer classifying lines +normalized to 48 pixels in height.

    +
    [1,48,0,1 Cr3,3,32 Do0.1,2 Mp2,2 Cr3,3,64 Do0.1,2 Mp2,2 S1(1x12)1,3 Lbx100 Do 01c59]
+
+Creating new model [1,48,0,1 Cr3,3,32 Do0.1,2 Mp2,2 Cr3,3,64 Do0.1,2 Mp2,2 S1(1x12)1,3 Lbx100 Do] with 59 outputs
+layer           type    params
+0               conv    kernel 3 x 3 filters 32 activation r
+1               dropout probability 0.1 dims 2
+2               maxpool kernel 2 x 2 stride 2 x 2
+3               conv    kernel 3 x 3 filters 64 activation r
+4               dropout probability 0.1 dims 2
+5               maxpool kernel 2 x 2 stride 2 x 2
+6               reshape from 1 1 x 12 to 1/3
+7               rnn     direction b transposed False summarize False out 100 legacy None
+8               dropout probability 0.5 dims 1
+9               linear  augmented False out 59
+
    +
    +

    A model with a small convolutional stack before a recurrent LSTM layer. The +extended dropout layer syntax is used to reduce drop probability on the depth +dimension as the default is too high for convolutional layers. The remainder of +the height dimension (12) is reshaped into the depth dimensions before +applying the final recurrent and linear layers.

    +
    [1,0,0,3 Cr3,3,16 Mp3,3 Lfys64 Lbx128 Lbx256 Do 01c59]
+
+Creating new model [1,0,0,3 Cr3,3,16 Mp3,3 Lfys64 Lbx128 Lbx256 Do] with 59 outputs
+layer           type    params
+0               conv    kernel 3 x 3 filters 16 activation r
+1               maxpool kernel 3 x 3 stride 3 x 3
+2               rnn     direction f transposed True summarize True out 64 legacy None
+3               rnn     direction b transposed False summarize False out 128 legacy None
+4               rnn     direction b transposed False summarize False out 256 legacy None
+5               dropout probability 0.5 dims 1
+6               linear  augmented False out 59
+
    +
    +

    A model with arbitrary sized color image input, an initial summarizing +recurrent layer to squash the height to 64, followed by 2 bi-directional +recurrent layers and a linear projection.

    +
    +
    +

    Convolutional Layers

    +
    C[{name}](s|t|r|l|m)[{name}]<y>,<x>,<d>
+s = sigmoid
+t = tanh
+r = relu
+l = linear
+m = softmax
+
    +
    +

    Adds a 2D convolution with kernel size (y, x) and d output channels, applying +the selected nonlinearity.

    +
    +
    +

    Recurrent Layers

    +
    L[{name}](f|r|b)(x|y)[s][{name}]<n> LSTM cell with n outputs.
+G[{name}](f|r|b)(x|y)[s][{name}]<n> GRU cell with n outputs.
+f runs the RNN forward only.
+r runs the RNN reversed only.
+b runs the RNN bidirectionally.
+s (optional) summarizes the output in the requested dimension, return the last step.
+
    +
    +

    Adds either an LSTM or GRU recurrent layer to the network using eiter the x +(width) or y (height) dimension as the time axis. Input features are the +channel dimension and the non-time-axis dimension (height/width) is treated as +another batch dimension. For example, a Lfx25 layer on an 1, 16, 906, 32 +input will execute 16 independent forward passes on 906x32 tensors resulting +in an output of shape 1, 16, 906, 25. If this isn’t desired either run a +summarizing layer in the other direction, e.g. Lfys20 for an input 1, 1, +906, 20, or prepend a reshape layer S1(1x16)1,3 combining the height and +channel dimension for an 1, 1, 906, 512 input to the recurrent layer.

    +
    +
    +

    Helper and Plumbing Layers

    +
    +

    Max Pool

    +
    Mp[{name}]<y>,<x>[,<y_stride>,<x_stride>]
+
    +
    +

    Adds a maximum pooling with (y, x) kernel_size and (y_stride, x_stride) stride.

    +
    +
    +

    Reshape

    +
    S[{name}]<d>(<a>x<b>)<e>,<f> Splits one dimension, moves one part to another
+        dimension.
+
    +
    +

    The S layer reshapes a source dimension d to a,b and distributes a into +dimension e, respectively b into f. Either e or f has to be equal to +d. So S1(1, 48)1, 3 on an 1, 48, 1020, 8 input will first reshape into +1, 1, 48, 1020, 8, leave the 1 part in the height dimension and distribute +the 48 sized tensor into the channel dimension resulting in a 1, 1, 1024, +48*8=384 sized output. S layers are mostly used to remove undesirable non-1 +height before a recurrent layer.

    +
    +

    Note

    +

    This S layer is equivalent to the one implemented in the tensorflow +implementation of VGSL, i.e. behaves differently from tesseract.

    +
    +
    +
    +
    +

    Regularization Layers

    +
    Do[{name}][<prob>],[<dim>] Insert a 1D or 2D dropout layer
+
    +
    +

    Adds an 1D or 2D dropout layer with a given probability. Defaults to 0.5 drop +probability and 1D dropout. Set to dim to 2 after convolutional layers.

    +
    +
    + + +
    + +
    +
    + +
    +
    +
    + ©2015-2024, Benjamin Kiessling. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + + | + Page source +
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module for the documentation. + */ +const Documentation = { + init: () => { + Documentation.initDomainIndexTable(); + Documentation.initOnKeyListeners(); + }, + + /** + * i18n support + */ + TRANSLATIONS: {}, + PLURAL_EXPR: (n) => (n === 1 ? 0 : 1), + LOCALE: "unknown", + + // gettext and ngettext don't access this so that the functions + // can safely bound to a different name (_ = Documentation.gettext) + gettext: (string) => { + const translated = Documentation.TRANSLATIONS[string]; + switch (typeof translated) { + case "undefined": + return string; // no translation + case "string": + return translated; // translation exists + default: + return translated[0]; // (singular, plural) translation tuple exists + } + }, + + ngettext: (singular, plural, n) => { + const translated = Documentation.TRANSLATIONS[singular]; + if (typeof translated !== "undefined") + return translated[Documentation.PLURAL_EXPR(n)]; + return n === 1 ? singular : plural; + }, + + addTranslations: (catalog) => { + 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a/2.0.0/_static/kraken_torchseqrecognizer.svg b/2.0.0/_static/kraken_torchseqrecognizer.svg new file mode 100644 index 000000000..c9a2f1135 --- /dev/null +++ b/2.0.0/_static/kraken_torchseqrecognizer.svg @@ -0,0 +1,239 @@ + + + + + + + + + + + + + Transcription Model + (TorchSeqRecognizer) + + + + + + + + + + Codec + + + + + + + + + + + Metadata + + + + + + + + + + + CTC Decoder + + + + + + + + + + + Neural Network + + + + diff --git a/2.0.0/_static/kraken_workflow.svg b/2.0.0/_static/kraken_workflow.svg new file mode 100644 index 000000000..5a50b51d6 --- /dev/null +++ b/2.0.0/_static/kraken_workflow.svg @@ -0,0 +1,753 @@ + + + + + + + + + + + + + + + Segmentation + + + + + + + + + + + Recognition + + + + + + + + + + + Serialization + + + + + + + + + + + + + + + + + + + + + + Recognition Model + + + + + + + + + + + + + + + + + + + + + + Segmentation Model + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + OCR Records + + + + + + + + + + + + + + + + + + Baselines, + Regions, + and Order + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Output File + + + + + + + + + + + + + + + + + + Output Template + + + + + + + + + + + + + + + + + + Image + + diff --git a/2.0.0/_static/language_data.js b/2.0.0/_static/language_data.js new file mode 100644 index 000000000..367b8ed81 --- /dev/null +++ b/2.0.0/_static/language_data.js @@ -0,0 +1,199 @@ +/* + * language_data.js + * ~~~~~~~~~~~~~~~~ + * + * This script contains the language-specific data used by searchtools.js, + * namely the list of stopwords, stemmer, scorer and splitter. + * + * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ + +var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"]; + + +/* Non-minified version is copied as a separate JS file, if available */ + +/** + * Porter Stemmer + */ +var Stemmer = function() { + + var step2list = { + ational: 'ate', + tional: 'tion', + enci: 'ence', + anci: 'ance', + izer: 'ize', + bli: 'ble', + alli: 'al', + entli: 'ent', + eli: 'e', + ousli: 'ous', + ization: 'ize', + ation: 'ate', + ator: 'ate', + alism: 'al', + iveness: 'ive', + fulness: 'ful', + ousness: 'ous', + aliti: 'al', + iviti: 'ive', + biliti: 'ble', + logi: 'log' + }; + + var step3list = { + icate: 'ic', + ative: '', + alize: 'al', + iciti: 'ic', + ical: 'ic', + ful: '', + ness: '' + }; + + var c = "[^aeiou]"; // consonant + var v = "[aeiouy]"; // vowel + var C = c + "[^aeiouy]*"; // consonant sequence + var V = v + "[aeiou]*"; // vowel sequence + + var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0 + var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1 + var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1 + var s_v = "^(" + C + ")?" + v; // vowel in stem + + this.stemWord = function (w) { + var stem; + var suffix; + var firstch; + var origword = w; + + if (w.length < 3) + return w; + + var re; + var re2; + var re3; + var re4; + + firstch = w.substr(0,1); + if (firstch == "y") + w = firstch.toUpperCase() + w.substr(1); + + // Step 1a + re = /^(.+?)(ss|i)es$/; + re2 = /^(.+?)([^s])s$/; + + if (re.test(w)) + w = w.replace(re,"$1$2"); + else if (re2.test(w)) + w = w.replace(re2,"$1$2"); + + // Step 1b + re = /^(.+?)eed$/; + re2 = /^(.+?)(ed|ing)$/; + if (re.test(w)) { + var fp = re.exec(w); + re = new RegExp(mgr0); + if (re.test(fp[1])) { + re = /.$/; + w = w.replace(re,""); + } + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1]; + re2 = new RegExp(s_v); + if (re2.test(stem)) { + w = stem; + re2 = /(at|bl|iz)$/; + re3 = new RegExp("([^aeiouylsz])\\1$"); + re4 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re2.test(w)) + w = w + "e"; + else if (re3.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + else if (re4.test(w)) + w = w + "e"; + } + } + + // Step 1c + re = /^(.+?)y$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(s_v); + if (re.test(stem)) + w = stem + "i"; + } + + // Step 2 + re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step2list[suffix]; + } + + // Step 3 + re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step3list[suffix]; + } + + // Step 4 + re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; + re2 = /^(.+?)(s|t)(ion)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + if (re.test(stem)) + w = stem; 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"undefined") { + var Scorer = { + // Implement the following function to further tweak the score for each result + // The function takes a result array [docname, title, anchor, descr, score, filename] + // and returns the new score. + /* + score: result => { + const [docname, title, anchor, descr, score, filename] = result + return score + }, + */ + + // query matches the full name of an object + objNameMatch: 11, + // or matches in the last dotted part of the object name + objPartialMatch: 6, + // Additive scores depending on the priority of the object + objPrio: { + 0: 15, // used to be importantResults + 1: 5, // used to be objectResults + 2: -5, // used to be unimportantResults + }, + // Used when the priority is not in the mapping. + objPrioDefault: 0, + + // query found in title + title: 15, + partialTitle: 7, + // query found in terms + term: 5, + partialTerm: 2, + }; +} + +const _removeChildren = (element) => { + while (element && element.lastChild) element.removeChild(element.lastChild); +}; + +/** + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping + */ +const _escapeRegExp = (string) => + string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string + +const _displayItem = (item, searchTerms, highlightTerms) => { + const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; + const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; + const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; + const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; + const contentRoot = document.documentElement.dataset.content_root; + + const [docName, title, anchor, descr, score, _filename] = item; + + let listItem = document.createElement("li"); + let requestUrl; + let linkUrl; + if (docBuilder === "dirhtml") { + // dirhtml builder + let dirname = docName + "/"; + if (dirname.match(/\/index\/$/)) + dirname = dirname.substring(0, dirname.length - 6); + else if (dirname === "index/") dirname = ""; + requestUrl = contentRoot + dirname; + linkUrl = requestUrl; + } else { + // normal html builders + requestUrl = contentRoot + docName + docFileSuffix; + linkUrl = docName + docLinkSuffix; + } + let linkEl = listItem.appendChild(document.createElement("a")); + linkEl.href = linkUrl + anchor; + linkEl.dataset.score = score; + linkEl.innerHTML = title; + if (descr) { + listItem.appendChild(document.createElement("span")).innerHTML = + " (" + descr + ")"; + // highlight search terms in the description + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + } + else if (showSearchSummary) + fetch(requestUrl) + .then((responseData) => responseData.text()) + .then((data) => { + if (data) + listItem.appendChild( + Search.makeSearchSummary(data, searchTerms, anchor) + ); + // highlight search terms in the summary + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + }); + Search.output.appendChild(listItem); +}; +const _finishSearch = (resultCount) => { + Search.stopPulse(); + Search.title.innerText = _("Search Results"); + if (!resultCount) + Search.status.innerText = Documentation.gettext( + "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." + ); + else + Search.status.innerText = _( + "Search finished, found ${resultCount} page(s) matching the search query." + ).replace('${resultCount}', resultCount); +}; +const _displayNextItem = ( + results, + resultCount, + searchTerms, + highlightTerms, +) => { + // results left, load the summary and display it + // this is intended to be dynamic (don't sub resultsCount) + if (results.length) { + _displayItem(results.pop(), searchTerms, highlightTerms); + setTimeout( + () => _displayNextItem(results, resultCount, searchTerms, highlightTerms), + 5 + ); + } + // search finished, update title and status message + else _finishSearch(resultCount); +}; +// Helper function used by query() to order search results. +// Each input is an array of [docname, title, anchor, descr, score, filename]. +// Order the results by score (in opposite order of appearance, since the +// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically. +const _orderResultsByScoreThenName = (a, b) => { + const leftScore = a[4]; + const rightScore = b[4]; + if (leftScore === rightScore) { + // same score: sort alphabetically + const leftTitle = a[1].toLowerCase(); + const rightTitle = b[1].toLowerCase(); + if (leftTitle === rightTitle) return 0; + return leftTitle > rightTitle ? -1 : 1; // inverted is intentional + } + return leftScore > rightScore ? 1 : -1; +}; + +/** + * Default splitQuery function. Can be overridden in ``sphinx.search`` with a + * custom function per language. + * + * The regular expression works by splitting the string on consecutive characters + * that are not Unicode letters, numbers, underscores, or emoji characters. + * This is the same as ``\W+`` in Python, preserving the surrogate pair area. + */ +if (typeof splitQuery === "undefined") { + var splitQuery = (query) => query + .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) + .filter(term => term) // remove remaining empty strings +} + +/** + * Search Module + */ +const Search = { + _index: null, + _queued_query: null, + _pulse_status: -1, + + htmlToText: (htmlString, anchor) => { + const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); + for (const removalQuery of [".headerlinks", "script", "style"]) { + htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() }); + } + if (anchor) { + const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`); + if (anchorContent) return anchorContent.textContent; + + console.warn( + `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.` + ); + } + + // if anchor not specified or not found, fall back to main content + const docContent = htmlElement.querySelector('[role="main"]'); + if (docContent) return docContent.textContent; + + console.warn( + "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template." + ); + return ""; + }, + + init: () => { + const query = new URLSearchParams(window.location.search).get("q"); + document + .querySelectorAll('input[name="q"]') + .forEach((el) => (el.value = query)); + if (query) Search.performSearch(query); + }, + + loadIndex: (url) => + (document.body.appendChild(document.createElement("script")).src = url), + + setIndex: (index) => { + Search._index = index; + if (Search._queued_query !== null) { + const query = Search._queued_query; + Search._queued_query = null; + Search.query(query); + } + }, + + hasIndex: () => Search._index !== null, + + deferQuery: (query) => (Search._queued_query = query), + + stopPulse: () => (Search._pulse_status = -1), + + startPulse: () => { + if (Search._pulse_status >= 0) return; + + const pulse = () => { + Search._pulse_status = (Search._pulse_status + 1) % 4; + Search.dots.innerText = ".".repeat(Search._pulse_status); + if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); + }; + pulse(); + }, + + /** + * perform a search for something (or wait until index is loaded) + */ + performSearch: (query) => { + // create the required interface elements + const searchText = document.createElement("h2"); + searchText.textContent = _("Searching"); + const searchSummary = document.createElement("p"); + searchSummary.classList.add("search-summary"); + searchSummary.innerText = ""; + const searchList = document.createElement("ul"); + searchList.classList.add("search"); + + const out = document.getElementById("search-results"); + Search.title = out.appendChild(searchText); + Search.dots = Search.title.appendChild(document.createElement("span")); + Search.status = out.appendChild(searchSummary); + Search.output = out.appendChild(searchList); + + const searchProgress = document.getElementById("search-progress"); + // Some themes don't use the search progress node + if (searchProgress) { + searchProgress.innerText = _("Preparing search..."); + } + Search.startPulse(); + + // index already loaded, the browser was quick! + if (Search.hasIndex()) Search.query(query); + else Search.deferQuery(query); + }, + + _parseQuery: (query) => { + // stem the search terms and add them to the correct list + const stemmer = new Stemmer(); + const searchTerms = new Set(); + const excludedTerms = new Set(); + const highlightTerms = new Set(); + const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); + splitQuery(query.trim()).forEach((queryTerm) => { + const queryTermLower = queryTerm.toLowerCase(); + + // maybe skip this "word" + // stopwords array is from language_data.js + if ( + stopwords.indexOf(queryTermLower) !== -1 || + queryTerm.match(/^\d+$/) + ) + return; + + // stem the word + let word = stemmer.stemWord(queryTermLower); + // select the correct list + if (word[0] === "-") excludedTerms.add(word.substr(1)); + else { + searchTerms.add(word); + highlightTerms.add(queryTermLower); + } + }); + + if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js + localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) + } + + // console.debug("SEARCH: searching for:"); + // console.info("required: ", [...searchTerms]); + // console.info("excluded: ", [...excludedTerms]); + + return [query, searchTerms, excludedTerms, highlightTerms, objectTerms]; + }, + + /** + * execute search (requires search index to be loaded) + */ + _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + const allTitles = Search._index.alltitles; + const indexEntries = Search._index.indexentries; + + // Collect multiple result groups to be sorted separately and then ordered. + // Each is an array of [docname, title, anchor, descr, score, filename]. + const normalResults = []; + const nonMainIndexResults = []; + + _removeChildren(document.getElementById("search-progress")); + + const queryLower = query.toLowerCase().trim(); + for (const [title, foundTitles] of Object.entries(allTitles)) { + if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) { + for (const [file, id] of foundTitles) { + let score = Math.round(100 * queryLower.length / title.length) + normalResults.push([ + docNames[file], + titles[file] !== title ? `${titles[file]} > ${title}` : title, + id !== null ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // search for explicit entries in index directives + for (const [entry, foundEntries] of Object.entries(indexEntries)) { + if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { + for (const [file, id, isMain] of foundEntries) { + const score = Math.round(100 * queryLower.length / entry.length); + const result = [ + docNames[file], + titles[file], + id ? "#" + id : "", + null, + score, + filenames[file], + ]; + if (isMain) { + normalResults.push(result); + } else { + nonMainIndexResults.push(result); + } + } + } + } + + // lookup as object + objectTerms.forEach((term) => + normalResults.push(...Search.performObjectSearch(term, objectTerms)) + ); + + // lookup as search terms in fulltext + normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms)); + + // let the scorer override scores with a custom scoring function + if (Scorer.score) { + normalResults.forEach((item) => (item[4] = Scorer.score(item))); + nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item))); + } + + // Sort each group of results by score and then alphabetically by name. + normalResults.sort(_orderResultsByScoreThenName); + nonMainIndexResults.sort(_orderResultsByScoreThenName); + + // Combine the result groups in (reverse) order. + // Non-main index entries are typically arbitrary cross-references, + // so display them after other results. + let results = [...nonMainIndexResults, ...normalResults]; + + // remove duplicate search results + // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept + let seen = new Set(); + results = results.reverse().reduce((acc, result) => { + let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); + if (!seen.has(resultStr)) { + acc.push(result); + seen.add(resultStr); + } + return acc; + }, []); + + return results.reverse(); + }, + + query: (query) => { + const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query); + const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms); + + // for debugging + //Search.lastresults = results.slice(); // a copy + // console.info("search results:", Search.lastresults); + + // print the results + _displayNextItem(results, results.length, searchTerms, highlightTerms); + }, + + /** + * search for object names + */ + performObjectSearch: (object, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const objects = Search._index.objects; + const objNames = Search._index.objnames; + const titles = Search._index.titles; + + const results = []; + + const objectSearchCallback = (prefix, match) => { + const name = match[4] + const fullname = (prefix ? prefix + "." : "") + name; + const fullnameLower = fullname.toLowerCase(); + if (fullnameLower.indexOf(object) < 0) return; + + let score = 0; + const parts = fullnameLower.split("."); + + // check for different match types: exact matches of full name or + // "last name" (i.e. last dotted part) + if (fullnameLower === object || parts.slice(-1)[0] === object) + score += Scorer.objNameMatch; + else if (parts.slice(-1)[0].indexOf(object) > -1) + score += Scorer.objPartialMatch; // matches in last name + + const objName = objNames[match[1]][2]; + const title = titles[match[0]]; + + // If more than one term searched for, we require other words to be + // found in the name/title/description + const otherTerms = new Set(objectTerms); + otherTerms.delete(object); + if (otherTerms.size > 0) { + const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); + if ( + [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) + ) + return; + } + + let anchor = match[3]; + if (anchor === "") anchor = fullname; + else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; + + const descr = objName + _(", in ") + title; + + // add custom score for some objects according to scorer + if (Scorer.objPrio.hasOwnProperty(match[2])) + score += Scorer.objPrio[match[2]]; + else score += Scorer.objPrioDefault; + + results.push([ + docNames[match[0]], + fullname, + "#" + anchor, + descr, + score, + filenames[match[0]], + ]); + }; + Object.keys(objects).forEach((prefix) => + objects[prefix].forEach((array) => + objectSearchCallback(prefix, array) + ) + ); + return results; + }, + + /** + * search for full-text terms in the index + */ + performTermsSearch: (searchTerms, excludedTerms) => { + // prepare search + const terms = Search._index.terms; + const titleTerms = Search._index.titleterms; + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + + const scoreMap = new Map(); + const fileMap = new Map(); + + // perform the search on the required terms + searchTerms.forEach((word) => { + const files = []; + const arr = [ + { files: terms[word], score: Scorer.term }, + { files: titleTerms[word], score: Scorer.title }, + ]; + // add support for partial matches + if (word.length > 2) { + const escapedWord = _escapeRegExp(word); + if (!terms.hasOwnProperty(word)) { + Object.keys(terms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: terms[term], score: Scorer.partialTerm }); + }); + } + if (!titleTerms.hasOwnProperty(word)) { + Object.keys(titleTerms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: titleTerms[term], score: Scorer.partialTitle }); + }); + } + } + + // no match but word was a required one + if (arr.every((record) => record.files === undefined)) return; + + // found search word in contents + arr.forEach((record) => { + if (record.files === undefined) return; + + let recordFiles = record.files; + if (recordFiles.length === undefined) recordFiles = [recordFiles]; + files.push(...recordFiles); + + // set score for the word in each file + recordFiles.forEach((file) => { + if (!scoreMap.has(file)) scoreMap.set(file, {}); + scoreMap.get(file)[word] = record.score; + }); + }); + + // create the mapping + files.forEach((file) => { + if (!fileMap.has(file)) fileMap.set(file, [word]); + else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word); + }); + }); + + // now check if the files don't contain excluded terms + const results = []; + for (const [file, wordList] of fileMap) { + // check if all requirements are matched + + // as search terms with length < 3 are discarded + const filteredTermCount = [...searchTerms].filter( + (term) => term.length > 2 + ).length; + if ( + wordList.length !== searchTerms.size && + wordList.length !== filteredTermCount + ) + continue; + + // ensure that none of the excluded terms is in the search result + if ( + [...excludedTerms].some( + (term) => + terms[term] === file || + titleTerms[term] === file || + (terms[term] || []).includes(file) || + (titleTerms[term] || []).includes(file) + ) + ) + break; + + // select one (max) score for the file. + const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w])); + // add result to the result list + results.push([ + docNames[file], + titles[file], + "", + null, + score, + filenames[file], + ]); + } + return results; + }, + + /** + * helper function to return a node containing the + * search summary for a given text. keywords is a list + * of stemmed words. + */ + makeSearchSummary: (htmlText, keywords, anchor) => { + const text = Search.htmlToText(htmlText, anchor); + if (text === "") return null; + + const textLower = text.toLowerCase(); + const actualStartPosition = [...keywords] + .map((k) => textLower.indexOf(k.toLowerCase())) + .filter((i) => i > -1) + .slice(-1)[0]; + const startWithContext = Math.max(actualStartPosition - 120, 0); + + const top = startWithContext === 0 ? "" : "..."; + const tail = startWithContext + 240 < text.length ? "..." : ""; + + let summary = document.createElement("p"); + summary.classList.add("context"); + summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; + + return summary; + }, +}; + +_ready(Search.init); diff --git a/2.0.0/_static/sphinx_highlight.js b/2.0.0/_static/sphinx_highlight.js new file mode 100644 index 000000000..8a96c69a1 --- /dev/null +++ b/2.0.0/_static/sphinx_highlight.js @@ -0,0 +1,154 @@ +/* Highlighting utilities for Sphinx HTML documentation. */ +"use strict"; + +const SPHINX_HIGHLIGHT_ENABLED = true + +/** + * highlight a given string on a node by wrapping it in + * span elements with the given class name. + */ +const _highlight = (node, addItems, text, className) => { + if (node.nodeType === Node.TEXT_NODE) { + const val = node.nodeValue; + const parent = node.parentNode; + const pos = val.toLowerCase().indexOf(text); + if ( + pos >= 0 && + !parent.classList.contains(className) && + !parent.classList.contains("nohighlight") + ) { + let span; + + const closestNode = parent.closest("body, svg, foreignObject"); + const isInSVG = closestNode && closestNode.matches("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.classList.add(className); + } + + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + const rest = document.createTextNode(val.substr(pos + text.length)); + parent.insertBefore( + span, + parent.insertBefore( + rest, + node.nextSibling + ) + ); + node.nodeValue = val.substr(0, pos); + /* There may be more occurrences of search term in this node. So call this + * function recursively on the remaining fragment. + */ + _highlight(rest, addItems, text, className); + + if (isInSVG) { + const rect = document.createElementNS( + "http://www.w3.org/2000/svg", + "rect" + ); + const bbox = parent.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute("class", className); + addItems.push({ parent: parent, target: rect }); + } + } + } else if (node.matches && !node.matches("button, select, textarea")) { + node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); + } +}; +const _highlightText = (thisNode, text, className) => { + let addItems = []; + _highlight(thisNode, addItems, text, className); + addItems.forEach((obj) => + obj.parent.insertAdjacentElement("beforebegin", obj.target) + ); +}; + +/** + * Small JavaScript module for the documentation. + */ +const SphinxHighlight = { + + /** + * highlight the search words provided in localstorage in the text + */ + highlightSearchWords: () => { + if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight + + // get and clear terms from localstorage + const url = new URL(window.location); + const highlight = + localStorage.getItem("sphinx_highlight_terms") + || url.searchParams.get("highlight") + || ""; + localStorage.removeItem("sphinx_highlight_terms") + url.searchParams.delete("highlight"); + window.history.replaceState({}, "", url); + + // get individual terms from highlight string + const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); + if (terms.length === 0) return; // nothing to do + + // There should never be more than one element matching "div.body" + const divBody = document.querySelectorAll("div.body"); + const body = divBody.length ? divBody[0] : document.querySelector("body"); + window.setTimeout(() => { + terms.forEach((term) => _highlightText(body, term, "highlighted")); + }, 10); + + const searchBox = document.getElementById("searchbox"); + if (searchBox === null) return; + searchBox.appendChild( + document + .createRange() + .createContextualFragment( + '

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+============== + +Optical character recognition is the serial execution of multiple steps, in the +case of kraken binarization (converting color and grayscale images into bitonal +ones), layout analysis/page segmentation (extracting topological text lines +from an image), recognition (feeding text lines images into an classifiers), +and finally serialization of results into an appropriate format such as hOCR or +ALTO. + +Input Specification +------------------- + +All kraken subcommands operating on input-output pairs, i.e. producing one +output document for one input document follow the basic syntax: + +.. code-block:: console + + $ kraken -i input_1 output_1 -i input_2 output_2 ... subcommand_1 subcommand_2 ... subcommand_n + +In particular subcommands may be chained. + +Binarization +------------ + +The binarization subcommand accepts almost the same parameters as +``ocropus-nlbin``. Only options not related to binarization, e.g. skew +detection are missing. In addition, error checking (image sizes, inversion +detection, grayscale enforcement) is always disabled and kraken will happily +binarize any image that is thrown at it. + +Available parameters are: + +=========== ==== +option type +=========== ==== +--threshold FLOAT +--zoom FLOAT +--escale FLOAT +--border FLOAT +--perc INTEGER RANGE +--range INTEGER +--low INTEGER RANGE +--high INTEGER RANGE +=========== ==== + +Page Segmentation and Script Detection +-------------------------------------- + +The `segment` subcommand access two operations page segmentation into lines and +script detection of those lines. + +Page segmentation is mostly parameterless, although a switch to change the +color of column separators has been retained. The segmentation is written as a +`JSON `_ file containing bounding boxes in reading order and +the general text direction (horizontal, i.e. LTR or RTL text in top-to-bottom +reading order or vertical-ltr/rtl for vertical lines read from left-to-right or +right-to-left). + +The script detection splits extracted lines from the segmenter into strip +sharing a particular script that can then be recognized by supplying +appropriate models for each detected script to the `ocr` subcommand. + +Combined output from both consists of lists in the `boxes` field corresponding +to a topographical line and containing one or more bounding boxes of a +particular script. Identifiers are `ISO 15924 +`_ 4 character codes. + +.. code-block:: console + + $ kraken -i 14.tif lines.txt segment + $ cat lines.json + { + "boxes" : [ + [ + ["Grek", [561, 216, 1626,309]] + ], + [ + ["Latn", [2172, 197, 2424, 244]] + ], + [ + ["Grek", [1678, 221, 2236, 320]], + ["Arab", [2241, 221, 2302, 320]] + ], + + ["Grek", [412, 318, 2215, 416]], + ["Latn", [2208, 318, 2424, 416]] + ], + ... + ], + "text_direction" : "horizontal-tb" + } + +Script detection is automatically enabled; by explicitly disabling script +detection the `boxes` field will contain only a list of line bounding boxes: + +.. code-block:: console + + [546, 216, 1626, 309], + [2169, 197, 2423, 244], + [1676, 221, 2293, 320], + ... + [503, 2641, 848, 2681] + +Available page segmentation parameters are: + +=============================================== ====== +option action +=============================================== ====== +-d, --text-direction Sets principal text direction. Valid values are `horizontal-lr`, `horizontal-rl`, `vertical-lr`, and `vertical-rl`. +--scale FLOAT Estimate of the average line height on the page +-m, --maxcolseps Maximum number of columns in the input document. Set to `0` for uni-column layouts. +-b, --black-colseps / -w, --white-colseps Switch to black column separators. +-r, --remove-hlines / -l, --hlines Disables prefiltering of small horizontal lines. Improves segmenter output on some Arabic texts. +=============================================== ====== + +The parameters specific to the script identification are: + +=============================================== ====== +option action +=============================================== ====== +-s/-n Enables/disables script detection +-a, --allowed-script Whitelists specific scripts for detection output. Other detected script runs are merged with their adjacent scripts, after a heuristic pre-merging step. +=============================================== ====== + +Model Repository +---------------- + +There is a semi-curated `repository +`_ of freely licensed recognition +models that can be accessed from the command line using a few subcommands. For +evaluating a series of models it is also possible to just clone the repository +using the normal git client. + +The ``list`` subcommand retrieves a list of all models available and prints +them including some additional information (identifier, type, and a short +description): + +.. code-block:: console + + $ kraken list + Retrieving model list ✓ + default (pyrnn) - A converted version of en-default.pyrnn.gz + toy (clstm) - A toy model trained on 400 lines of the UW3 data set. + ... + +To access more detailed information the ``show`` subcommand may be used: + +.. code-block:: console + + $ kraken show toy + name: toy.clstm + + A toy model trained on 400 lines of the UW3 data set. + + author: Benjamin Kiessling (mittagessen@l.unchti.me) + http://kraken.re + +If a suitable model has been decided upon it can be retrieved using the ``get`` +subcommand: + +.. code-block:: console + + $ kraken get toy + Retrieving model ✓ + +Models will be placed in $XDG_BASE_DIR and can be accessed using their name as +shown by the ``show`` command, e.g.: + +.. code-block:: console + + $ kraken -i ... ... ocr -m toy + +Additions and updates to existing models are always welcome! Just open a pull +request or write an email. + +Recognition +----------- + +Recognition requires a grey-scale or binarized image, a page segmentation for +that image, and a model file. In particular there is no requirement to use the +page segmentation algorithm contained in the ``segment`` subcommand or the +binarization provided by kraken. + +Multi-script recognition is possible by supplying a script-annotated +segmentation and a mapping between scripts and models: + +.. code-block:: console + + $ kraken -i ... ... ocr -m Grek:porson.clstm -m Latn:antiqua.clstm + +All polytonic Greek text portions will be recognized using the `porson.clstm` +model while Latin text will be fed into the `antiqua.clstm` model. It is +possible to define a fallback model that other text will be fed to: + +.. code-block:: console + + $ kraken -i ... ... ocr -m ... -m ... -m default:porson.clstm + +It is also possible to disable recognition on a particular script by mapping to +the special model keyword `ignore`. Ignored lines will still be serialized but +will not contain any recognition results. + +The ``ocr`` subcommand is able to serialize the recognition results either as +plain text (default), as `hOCR `_, into `ALTO +`_, or abbyyXML containing additional +metadata such as bounding boxes and confidences: + +.. code-block:: console + + $ kraken -i ... ... ocr -t # text output + $ kraken -i ... ... ocr -h # hOCR output + $ kraken -i ... ... ocr -a # ALTO output + $ kraken -i ... ... ocr -y # abbyyXML output + +hOCR output is slightly different from hOCR files produced by ocropus. Each +``ocr_line`` span contains not only the bounding box of the line but also +character boxes (``x_bboxes`` attribute) indicating the coordinates of each +character. In each line alternating sequences of alphanumeric and +non-alphanumeric (in the unicode sense) characters are put into ``ocrx_word`` +spans. Both have bounding boxes as attributes and the recognition confidence +for each character in the ``x_conf`` attribute. + +Paragraph detection has been removed as it was deemed to be unduly dependent on +certain typographic features which may not be valid for your input. diff --git a/2.0.0/_sources/api.rst.txt b/2.0.0/_sources/api.rst.txt new file mode 100644 index 000000000..c133c8419 --- /dev/null +++ b/2.0.0/_sources/api.rst.txt @@ -0,0 +1,94 @@ +kraken API +========== + +.. module:: kraken + +Kraken provides routines which are usable by third party tools. In general +you can expect function in the ``kraken`` package to remain stable. We will try +to keep these backward compatible, but as kraken is still in an early +development stage and the API is still quite rudimentary nothing can be +garantueed. + +kraken.binarization module +-------------------------- + +.. automodule:: kraken.binarization + :members: + :show-inheritance: + +kraken.serialization module +--------------------------- + +.. automodule:: kraken.serialization + :members: + :show-inheritance: + +kraken.pageseg module +--------------------- + +.. automodule:: kraken.pageseg + :members: + :show-inheritance: + +kraken.rpred module +------------------- + +.. automodule:: kraken.rpred + :members: + :show-inheritance: + +kraken.transcribe module +------------------------ + +.. automodule:: kraken.transcribe + :members: + :show-inheritance: + +kraken.linegen module +--------------------- + +.. automodule:: kraken.linegen + :members: + :show-inheritance: + +kraken.lib.models module +------------------------ + +.. automodule:: kraken.lib.models + :members: + :show-inheritance: + +kraken.lib.vgsl module +---------------------- + +.. automodule:: kraken.lib.vgsl + :members: + :show-inheritance: + +kraken.lib.codec +---------------- + +.. automodule:: kraken.lib.codec + :members: + :show-inheritance: + +kraken.lib.train module +----------------------- + +.. automodule:: kraken.lib.train + :members: + :show-inheritance: + +kraken.lib.dataset module +------------------------- + +.. automodule:: kraken.lib.dataset + :members: + :show-inheritance: + +kraken.lib.ctc_decoder +---------------------- + +.. automodule:: kraken.lib.ctc_decoder + :members: + :show-inheritance: diff --git a/2.0.0/_sources/gpu.rst.txt b/2.0.0/_sources/gpu.rst.txt new file mode 100644 index 000000000..fbb66ba76 --- /dev/null +++ b/2.0.0/_sources/gpu.rst.txt @@ -0,0 +1,10 @@ +.. _gpu: + +GPU Acceleration +================ + +The latest version of kraken uses a new pytorch backend which enables GPU +acceleration both for training and recognition. Apart from a compatible Nvidia +GPU, CUDA and cuDNN have to be installed so pytorch can run computation on it. + + diff --git a/2.0.0/_sources/index.rst.txt b/2.0.0/_sources/index.rst.txt new file mode 100644 index 000000000..41c5f767b --- /dev/null +++ b/2.0.0/_sources/index.rst.txt @@ -0,0 +1,154 @@ +kraken +====== + +.. toctree:: + :hidden: + :maxdepth: 2 + + advanced + Training + API + Models + +kraken is a turn-key OCR system forked from `ocropus +`_. It is intended to rectify a number of +issues while preserving (mostly) functional equivalence. + +Features +======== + +kraken's main features are: + + - Script detection and multi-script recognition support + - `Right-to-Left `_, `BiDi + `_, and Top-to-Bottom + script support + - `ALTO `_, abbyXML, and hOCR output + - Word bounding boxes and character cuts + - `Public repository `_ of model files + - :ref:`Lightweight model files ` + - :ref:`Variable recognition network architectures ` + +All functionality not pertaining to OCR and prerequisite steps has been +removed, i.e. no more error rate measuring, etc. + +Pull requests and code contributions are always welcome. + +Installation +============ + +kraken requires some external libraries to run. On Debian/Ubuntu they may be +installed using: + +.. code-block:: console + + # apt install libpangocairo-1.0 libxml2 libblas3 liblapack3 python3-dev python3-pip + +pip +--- + +.. code-block:: console + + $ pip3 install kraken + +or by running pip in the git repository: + +.. code-block:: console + + $ pip3 install . + +conda +----- + +If you are running `Anaconda `_/miniconda, use: + +.. code-block:: console + + $ conda install -c mittagessen kraken + +Models +------ + +Finally you'll have to scrounge up a recognition model to do the actual +recognition of characters. To download the default English text recognition +model and place it in the user's kraken directory: + +.. code-block:: console + + $ kraken get default + +A list of libre models available in the central repository can be retrieved by +running: + +.. code-block:: console + + $ kraken list + +Model metadata can be extracted using: + +.. code-block:: console + + $ kraken show arabic-alam-al-kutub + name: arabic-alam-al-kutub.clstm + + An experimental model for Classical Arabic texts. + + Network trained on 889 lines of [0] as a test case for a general Classical + Arabic model. Ground truth was prepared by Sarah Savant + and Maxim Romanov . + + Vocalization was omitted in the ground truth. Training was stopped at ~35000 + iterations with an accuracy of 97%. + + [0] Ibn al-Faqīh (d. 365 AH). Kitāb al-buldān. Edited by Yūsuf al-Hādī, 1st + edition. Bayrūt: ʿĀlam al-kutub, 1416 AH/1996 CE. + alphabet: !()-.0123456789:[] «»،؟ءابةتثجحخدذرزسشصضطظعغفقكلمنهوىي ARABIC + MADDAH ABOVE, ARABIC HAMZA ABOVE, ARABIC HAMZA BELOW + +Quickstart +========== + +Recognizing text on an image using the default parameters including the +prerequisite steps of binarization and page segmentation: + +.. code-block:: console + + $ kraken -i image.tif image.txt binarize segment ocr + Loading RNN ✓ + Processing ⣻ + +To binarize a single image using the nlbin algorithm: + +.. code-block:: console + + $ kraken -i image.tif bw.tif binarize + +To segment a binarized image into reading-order sorted lines: + +.. code-block:: console + + $ kraken -i bw.tif lines.json segment + +To OCR a binarized image using the default RNN and the previously generated +page segmentation: + +.. code-block:: console + + $ kraken -i bw.tif image.txt ocr --lines lines.json + +All commands and their parameters are documented, just add the standard +``--help`` flag for further information. + +Training Tutorial +================= + +There is a training tutorial at :doc:`training`. + +.. _license: + +License +======= + +``Kraken`` is provided under the terms and conditions of the `Apache 2.0 +License `_ retained +from the original ``ocropus`` distribution. diff --git a/2.0.0/_sources/ketos.rst.txt b/2.0.0/_sources/ketos.rst.txt new file mode 100644 index 000000000..1af797490 --- /dev/null +++ b/2.0.0/_sources/ketos.rst.txt @@ -0,0 +1,519 @@ +.. _ketos: + +Training +======== + +This page describes the training utilities available through the ``ketos`` +command line utility in depth. For a gentle introduction on model training +please refer to the :ref:`tutorial `. + +Thanks to the magic of `Connectionist Temporal Classification +`_ prerequisites for creating a +new recognition model are quite modest. The basic requirement is a number of +text lines (``ground truth``) that correspond to line images and some time for +training. + +Transcription +------------- + +Transcription is done through local browser based HTML transcription +environments. These are created by the ``ketos transcribe`` command line util. +Its basic input is just a number of image files and an output path to write the +HTML file to: + +.. code-block:: console + + $ ketos transcribe -o output.html image_1.png image_2.png ... + +While it is possible to put multiple images into a single transcription +environment splitting into one-image-per-HTML will ease parallel transcription +by multiple people. + +The above command reads in the image files, converts them to black and white, +tries to split them into line images, and puts an editable text field next to +the image in the HTML. There are a handful of option changing the output: + +=============================================== ====== +option action +=============================================== ====== +-d, --text-direction Sets the principal text direction both for the segmenter and in the HTML. Can be one of horizontal-lr, horizontal-rl, vertical-lr, vertical-rl. +--scale A segmenter parameter giving an estimate of average line height. Usually it shouldn't be set manually. +--bw / --orig Disables binarization of input images. If color or grayscale training data is desired this option has to be set. +-m, --maxcolseps A segmenter parameter limiting the number of columns that can be found in the input image by setting the maximum number of column separators. Set to 0 to disable column detection. +-b, --black_colseps / -w, --white_colseps A segmenter parameter selecting white or black column separators. +-f, --font The font family to use for rendering the text in the HTML. +-fs, --font-style The font style to use in the HTML. +-p, --prefill A model to use for prefilling the transcription. (Optional) +-o, --output Output HTML file. +=============================================== ====== + +It is possible to use an existing model to prefill the transcription environments: + +.. code-block:: console + + $ ketos transcribe -p ~/arabic.mlmodel -p output.html image_1.png image_2.png ... + +Transcription has to be diplomatic, i.e. contain the exact character sequence +in the line image, including original orthography. Some deviations, such as +consistently omitting vocalization in Arabic texts, is possible as long as they +are systematic and relatively minor. + +After transcribing a number of lines the results have to be saved, either using +the ``Download`` button on the lower right or through the regular ``Save Page +As`` function of the browser. All the work done is contained directly in the +saved files and it is possible to save partially transcribed files and continue +work later. + +Next the contents of the filled transcription environments have to be +extracted through the ``ketos extract`` command: + +.. code-block:: console + + $ ketos extract --output output_directory *.html + + +There are some options dealing with color images and text normalization: + +======================================================= ====== +option action +======================================================= ====== +-b, --binarize / --no-binarize Binarizes color/grayscale images (default) or retains the original in the output. +-u, --normalization Normalizes text to one of the following Unicode normalization forms: NFD, NFKD, NFC, NFKC +-s, --normalize-whitespace / --no-normalize-whitespace Normalizes whitespace in extracted text. There are several different Unicode `whitespace + `_ characters that + are replaced by a standard space when not disabled. +--reorder / --no-reorder Tells ketos to reorder the code + point for each line into + left-to-right order. Unicode + code points are always in + reading order, e.g. the first + code point in an Arabic line + will be the rightmost + character. This option reorders + them into ``display order``, + i.e. the first code point is + the leftmost, the second one + the next from the left and so + on. The ``train`` subcommand + does this automatically, so it + usually isn't needed. +-r, --rotate / --no-rotate Skips rotation of vertical lines. +-o, --output Output directory, defaults to ``training`` +======================================================= ====== + +The result will be a directory filled with line image text pairs ``NNNNNN.png`` +and ``NNNNNN.gt.txt`` and a ``manifest.txt`` containing a list of all extracted +lines. + +Training +-------- + +The training utility allows training of :ref:`VGSL ` specified models +both from scratch and from existing models. Training data is in all cases just +a directory containing image-text file pairs as produced by the +``transcribe/extract`` tools. Here are its command line options: + +======================================================= ====== +option action +======================================================= ====== +-p, --pad Left and right padding around lines +-o, --output Output model file prefix. Defaults to model. +-s, --spec VGSL spec of the network to train. CTC layer + will be added automatically. default: + [1,48,0,1 Cr3,3,32 Do0.1,2 Mp2,2 Cr3,3,64 + Do0.1,2 Mp2,2 S1(1x12)1,3 Lbx100 Do] +-a, --append Removes layers before argument and then + appends spec. Only works when loading an + existing model +-i, --load Load existing file to continue training +-F, --savefreq Model save frequency in epochs during + training +-R, --report Report creation frequency in epochs +-q, --quit Stop condition for training. Set to `early` + for early stopping (default) or `dumb` for fixed + number of epochs. +-N, --epochs Number of epochs to train for. Set to -1 for indefinite training. +--lag Number of epochs to wait before stopping + training without improvement. Only used when using early stopping. +--min-delta Minimum improvement between epochs to reset + early stopping. Defaults to 0.005. +-d, --device Select device to use (cpu, cuda:0, cuda:1,...). GPU acceleration requires CUDA. +--optimizer Select optimizer (Adam, SGD, RMSprop). +-r, --lrate Learning rate [default: 0.001] +-m, --momentum Momentum used with SGD optimizer. Ignored otherwise. +-w, --weight-decay Weight decay. +--schedule Sets the learning rate scheduler. May be either constant or 1cycle. For 1cycle + the cycle length is determined by the `--epoch` option. +-p, --partition Ground truth data partition ratio between train/validation set +-u, --normalization Ground truth Unicode normalization. One of NFC, NFKC, NFD, NFKD. +-c, --codec Load a codec JSON definition (invalid if loading existing model) +--resize Codec/output layer resizing option. If set + to `add` code points will be added, `both` + will set the layer to match exactly the + training data, `fail` will abort if training + data and model codec do not match. Only valid when refining an existing model. +-n, --reorder / --no-reorder Reordering of code points to display order. +-t, --training-files File(s) with additional paths to training data. Used to + enforce an explicit train/validation set split and deal with + training sets with more lines than the command line can process. Can be used more than once. +-e, --evaluation-files File(s) with paths to evaluation data. Overrides the `-p` parameter. +--preload / --no-preload Hard enable/disable for training data preloading. Preloading + training data into memory is enabled per default for sets with less than 2500 lines. +--threads Number of OpenMP threads when running on CPU. Defaults to min(4, #cores). +======================================================= ====== + +From Scratch +~~~~~~~~~~~~ + +The absolut minimal example to train a new model is: + +.. code-block:: console + + $ ketos train training_data/*.png + +Training will continue until the error does not improve anymore and the best +model (among intermediate results) will be saved in the current directory. + +In some cases, such as color inputs, changing the network architecture might be +useful: + +.. code-block:: console + + $ ketos train -s '[1,0,0,3 Cr3,3,16 Mp3,3 Lfys64 Lbx128 Lbx256 Do]' syr/*.png + +Complete documentation for the network description language can be found on the +:ref:`VGSL ` page. + +Sometimes the early stopping default parameters might produce suboptimal +results such as stopping training too soon. Adjusting the minimum delta an/or +lag can be useful: + +.. code-block:: console + + $ ketos train --lag 10 --min-delta 0.001 syr/*.png + +To switch optimizers from Adam to SGD or RMSprop just set the option: + +.. code-block:: console + + $ ketos train --optimizer SGD syr/*.png + +It is possible to resume training from a previously saved model: + +.. code-block:: console + + $ ketos train -i model_25.mlmodel syr/*.png + +Fine Tuning +~~~~~~~~~~~ + +Fine tuning an existing model for another typeface or new characters is also +possible with the same syntax as resuming regular training: + +.. code-block:: console + + $ ketos train -i model_best.mlmodel syr/*.png + +The caveat is that the alphabet of the base model and training data have to be +an exact match. Otherwise an error will be raised: + +.. code-block:: console + + $ ketos train -i model_5.mlmodel --no-preload kamil/*.png + Building training set [####################################] 100% + Building validation set [####################################] 100% + [0.8616] alphabet mismatch {'~', '»', '8', '9', 'ـ'} + Network codec not compatible with training set + [0.8620] Training data and model codec alphabets mismatch: {'ٓ', '؟', '!', 'ص', '،', 'ذ', 'ة', 'ي', 'و', 'ب', 'ز', 'ح', 'غ', '~', 'ف', ')', 'د', 'خ', 'م', '»', 'ع', 'ى', 'ق', 'ش', 'ا', 'ه', 'ك', 'ج', 'ث', '(', 'ت', 'ظ', 'ض', 'ل', 'ط', '؛', 'ر', 'س', 'ن', 'ء', 'ٔ', '«', 'ـ', 'ٕ'} + +There are two modes dealing with mismatching alphabets, ``add`` and ``both``. +``add`` resizes the output layer and codec of the loaded model to include all +characters in the new training set without removing any characters. ``both`` +will make the resulting model an exact match with the new training set by both +removing unused characters from the model and adding new ones. + +.. code-block:: console + + $ ketos -v train --resize add -i model_5.mlmodel syr/*.png + ... + [0.7943] Training set 788 lines, validation set 88 lines, alphabet 50 symbols + ... + [0.8337] Resizing codec to include 3 new code points + [0.8374] Resizing last layer in network to 52 outputs + ... + +In this example 3 characters were added for a network that is able to +recognize 52 different characters after sufficient additional training. + +.. code-block:: console + + $ ketos -v train --resize both -i model_5.mlmodel syr/*.png + ... + [0.7593] Training set 788 lines, validation set 88 lines, alphabet 49 symbols + ... + [0.7857] Resizing network or given codec to 49 code sequences + [0.8344] Deleting 2 output classes from network (46 retained) + ... + +In ``both`` mode 2 of the original characters were removed and 3 new ones were added. + + +Slicing +~~~~~~~ + +Refining on mismatched alphabets has its limits. If the alphabets are highly +different the modification of the final linear layer to add/remove character +will destroy the inference capabilities of the network. In those cases it is +faster to slice off the last few layers of the network and only train those +instead of a complete network from scratch. + +Taking the default network definition as printed in the debug log we can see +the layer indices of the model: + +.. code-block:: console + + [0.8760] Creating new model [1,48,0,1 Cr3,3,32 Do0.1,2 Mp2,2 Cr3,3,64 Do0.1,2 Mp2,2 S1(1x12)1,3 Lbx100 Do] with 48 outputs + [0.8762] layer type params + [0.8790] 0 conv kernel 3 x 3 filters 32 activation r + [0.8795] 1 dropout probability 0.1 dims 2 + [0.8797] 2 maxpool kernel 2 x 2 stride 2 x 2 + [0.8802] 3 conv kernel 3 x 3 filters 64 activation r + [0.8804] 4 dropout probability 0.1 dims 2 + [0.8806] 5 maxpool kernel 2 x 2 stride 2 x 2 + [0.8813] 6 reshape from 1 1 x 12 to 1/3 + [0.8876] 7 rnn direction b transposed False summarize False out 100 legacy None + [0.8878] 8 dropout probability 0.5 dims 1 + [0.8883] 9 linear augmented False out 48 + +To remove everything after the initial convolutional stack and add untrained +layers we define a network stub and index for appending: + +.. code-block:: console + + $ ketos train -i model_1.mlmodel --append 7 -s '[Lbx256 Do]' syr/*.png + Building training set [####################################] 100% + Building validation set [####################################] 100% + [0.8014] alphabet mismatch {'8', '3', '9', '7', '܇', '݀', '݂', '4', ':', '0'} + Slicing and dicing model ✓ + +The new model will behave exactly like a new one, except potentially training a +lot faster. + +Testing +------- + +Picking a particular model from a pool or getting a more detailled look on the +recognition accuracy can be done with the `test` command. It uses transcribed +lines, the test set, in the same format as the `train` command, recognizes the +line images with one or more models, and creates a detailled report of the +differences from the ground truth for each of them. + +======================================================= ====== +option action +======================================================= ====== +-m, --model Model(s) to evaluate. +-e, --evaluation-files File(s) with paths to evaluation data. +-d, --device Select device to use. +-p, --pad Left and right padding around lines. + + +Transcriptions are handed to the command in the same way as for the `train` +command, either through a manifest with `-e/--evaluation-files` or by just +adding a number of image files as the final argument: + +.. code-block:: console + + $ ketos test -m $model -e test.txt test/*.png + Evaluating $model + Evaluating [####################################] 100% + === report test_model.mlmodel === + + 7012 Characters + 6022 Errors + 14.12% Accuracy + + 5226 Insertions + 2 Deletions + 794 Substitutions + + Count Missed %Right + 1567 575 63.31% Common + 5230 5230 0.00% Arabic + 215 215 0.00% Inherited + + Errors Correct-Generated + 773 { ا } - { } + 536 { ل } - { } + 328 { و } - { } + 274 { ي } - { } + 266 { م } - { } + 256 { ب } - { } + 246 { ن } - { } + 241 { SPACE } - { } + 207 { ر } - { } + 199 { ف } - { } + 192 { ه } - { } + 174 { ع } - { } + 172 { ARABIC HAMZA ABOVE } - { } + 144 { ت } - { } + 136 { ق } - { } + 122 { س } - { } + 108 { ، } - { } + 106 { د } - { } + 82 { ك } - { } + 81 { ح } - { } + 71 { ج } - { } + 66 { خ } - { } + 62 { ة } - { } + 60 { ص } - { } + 39 { ، } - { - } + 38 { ش } - { } + 30 { ا } - { - } + 30 { ن } - { - } + 29 { ى } - { } + 28 { ذ } - { } + 27 { ه } - { - } + 27 { ARABIC HAMZA BELOW } - { } + 25 { ز } - { } + 23 { ث } - { } + 22 { غ } - { } + 20 { م } - { - } + 20 { ي } - { - } + 20 { ) } - { } + 19 { : } - { } + 19 { ط } - { } + 19 { ل } - { - } + 18 { ، } - { . } + 17 { ة } - { - } + 16 { ض } - { } + ... + Average accuracy: 14.12%, (stddev: 0.00) + +The report(s) contains character accuracy measured per script and a detailled +list of confusions. When evaluating multiple models the last line of the output +will the average accuracy and the standard deviation across all of them. + +Artificial Training Data +------------------------ + +It is possible to rely on artificially created training data, instead of +laborously creating ground truth by manual means. A proper typeface and some +text in the target language will be needed. + +For many popular historical fonts there are free reproductions which quite +closely match printed editions. Most are available in your distribution's + +repositories and often shipped with TeX Live. + +Some good places to start for non-Latin scripts are: + +- `Amiri `_, a classical Arabic typeface by Khaled + Hosny +- The `Greek Font Society `_ offers freely + licensed (historical) typefaces for polytonic Greek. +- The friendly religious fanatics from `SIL `_ + assemble a wide variety of fonts for non-Latin scripts. + +Next we need some text to generate artificial line images from. It should be a +typical example of the type of printed works you want to recognize and at least +500-1000 lines in length. + +A minimal invocation to the line generation tool will look like this: + +.. code-block:: console + + $ ketos linegen -f Amiri da1.txt da2.txt + Reading texts ✓ + Read 3692 unique lines + Σ (len: 99) + Symbols: !(),-./0123456789:ABEFGHILMNPRS[]_acdefghiklmnoprstuvyz«»،؟ءآأؤإئابةتثجحخدذرزسشصضطظعغـفقكلمنهوىيپ + Writing images ✓ + +The output will be written to a directory called ``training_data``, although +this may be changed using the ``-o`` option. Each text line is rendered using +the Amiri typeface. + +Alphabet and Normalization +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Let's take a look at important information in the preamble: + +.. code-block:: console + + Read 3692 unique lines + Σ (len: 99) + Symbols: !(),-./0123456789:ABEFGHILMNPRS[]_acdefghiklmnoprstuvyz«»،؟ﺀﺁﺃﺅﺈﺋﺎﺑﺔﺘﺜﺠﺤﺧﺩﺫﺭﺰﺴﺸﺼﻀﻄﻈﻌﻐـﻔﻘﻜﻠﻤﻨﻫﻭﻰﻳپ + +ketos tells us that it found 3692 unique lines which contained 99 different +``symbols`` or ``code points``. We can see the training data contains all of +the Arabic script including accented precomposed characters, but only a subset +of Latin characters, numerals, and punctuation. A trained model will be able to +recognize only these exact symbols, e.g. a ``C`` or ``j`` on the page will +never be recognized. Either accept this limitation or add additional text lines +to the training corpus until the alphabet matches your needs. + +We can also force a normalization form using the ``-u`` option; per default +none is applied. For example: + +.. code-block:: console + + $ ketos linegen -u NFD -f "GFS Philostratos" grc.txt + Reading texts ✓ + Read 2860 unique lines + Σ (len: 132) + Symbols: #&'()*,-./0123456789:;ABCDEGHILMNOPQRSTVWXZ]abcdefghiklmnopqrstuvxy §·ΑΒΓΔΕΖΗΘΙΚΛΜΝΞΟΠΡΣΤΥΦΧΨΩαβγδεζηθικλμνξοπρςστυφχψω—‘’“ + Combining Characters: COMBINING GRAVE ACCENT, COMBINING ACUTE ACCENT, COMBINING DIAERESIS, COMBINING COMMA ABOVE, COMBINING REVERSED COMMA ABOVE, COMBINING DOT BELOW, COMBINING GREEK PERISPOMENI, COMBINING GREEK YPOGEGRAMMENI + + + $ ketos linegen -u NFC -f "GFS Philostratos" grc.txt + Reading texts ✓ + Read 2860 unique lines + Σ (len: 231) + Symbols: #&'()*,-./0123456789:;ABCDEGHILMNOPQRSTVWXZ]abcdefghiklmnopqrstuvxy §·ΐΑΒΓΔΕΖΘΙΚΛΜΝΞΟΠΡΣΤΦΧΨΩάέήίαβγδεζηθικλμνξοπρςστυφχψωϊϋόύώἀἁἂἃἄἅἈἌἎἐἑἓἔἕἘἙἜἝἠἡἢἣἤἥἦἧἩἭἮἰἱἳἴἵἶἷἸἹἼὀὁὂὃὄὅὈὉὌὐὑὓὔὕὖὗὙὝὠὡὢὤὥὦὧὨὩὰὲὴὶὸὺὼᾄᾐᾑᾔᾗᾠᾤᾧᾳᾶᾷῃῄῆῇῒῖῥῦῬῳῴῶῷ—‘’“ + Combining Characters: COMBINING ACUTE ACCENT, COMBINING DOT BELOW + +While there hasn't been any study on the effect of different normalizations on +recognition accuracy there are some benefits to NFD, namely decreased model +size and easier validation of the alphabet. + +Other Parameters +~~~~~~~~~~~~~~~~ + +Sometimes it is desirable to draw a certain number of lines randomly from one +or more large texts. The ``-n`` option does just that: + +.. code-block:: console + + $ ketos linegen -u NFD -n 100 -f Amiri da1.txt da2.txt da3.txt da4.txt + Reading texts ✓ + Read 114265 unique lines + Sampling 100 lines ✓ + Σ (len: 64) + Symbols: !(),-./0123456789:[]{}«»،؛؟ءابةتثجحخدذرزسشصضطظعغـفقكلمنهوىي– + Combining Characters: ARABIC MADDAH ABOVE, ARABIC HAMZA ABOVE, ARABIC HAMZA BELOW + Writing images ⢿ + +It is also possible to adjust to amount of degradation/distortion of line +images by using the ``-s/-r/-d/-ds`` switches: + +.. code-block:: console + + $ ketos linegen -m 0.2 -s 0.002 -r 0.001 -d 3 Downloads/D/A/da1.txt + Reading texts ✓ + Read 859 unique lines + Σ (len: 46) + Symbols: !"-.:،؛؟ءآأؤإئابةتثجحخدذرزسشصضطظعغفقكلمنهوىي + Writing images ⣽ + + +Sometimes the shaping engine misbehaves using some fonts (notably ``GFS +Philostratos``) by rendering texts in certain normalizations incorrectly if the +font does not contain glyphs for decomposed characters. One sign are misplaced +diacritics and glyphs in different fonts. A workaround is renormalizing the +text for rendering purposes (here to NFC): + +.. code-block:: console + + $ ketos linegen -ur NFC -u NFD -f "GFS Philostratos" grc.txt + + diff --git a/2.0.0/_sources/models.rst.txt b/2.0.0/_sources/models.rst.txt new file mode 100644 index 000000000..4ff0b7f90 --- /dev/null +++ b/2.0.0/_sources/models.rst.txt @@ -0,0 +1,58 @@ +.. _models: + +Models +====== + +There are currently three kinds of models containing the recurrent neural +networks doing all the character recognition supported by kraken: ``pronn`` +files serializing old pickled ``pyrnn`` models as protobuf, clstm's native +serialization, and versatile `Core ML +`_ models. + +.. _pyrnn: + +pyrnn +----- + +These are serialized instances of python ``lstm.SeqRecognizer`` objects. Using +such a model just entails loading the pickle and calling the appropriate +functions to perform recognition much like a shared library in other +programming languages. + +Support for these models has been dropped with kraken 1.0 as python 2.7 is +phased out. + +pronn +----- + +Legacy python models can be converted to a protobuf based serialization. These +are loadable by kraken 1.0 and will be automatically converted to Core ML. + +Protobuf models have several advantages over pickled ones. They are noticeably +smaller (80Mb vs 1.8Mb for the default model), don't allow arbitrary code +execution, and are upward compatible with python 3. Because they are so much +more lightweight they are also loaded much faster. + +clstm +----- + +`clstm `_, a small and fast implementation of +LSTM networks that was used in previous kraken versions. The model files can be +loaded with pytorch-based kraken and will be converted to Core ML. + +CoreML +------ + +Core ML allows arbitrary network architectures in a compact serialization with +metadata. This is the default format in pytorch-based kraken. + +Conversion +---------- + +Per default pronn/clstm models are automatically converted to the new Core ML +format when explicitely defined using the ``-m`` option to the ``ocr`` utility +on the command line. They are stored in the user kraken directory (default is +~/.kraken) and will be automatically substituted in future runs. + +If conversion is not desired, e.g. because there is a bug in the conversion +routine, it can be disabled using the ``--disable-autoconversion`` switch. diff --git a/2.0.0/_sources/training.rst.txt b/2.0.0/_sources/training.rst.txt new file mode 100644 index 000000000..82f6ec97b --- /dev/null +++ b/2.0.0/_sources/training.rst.txt @@ -0,0 +1,513 @@ +.. _training: + +Training a kraken model +======================= + +kraken is an optical character recognition package that can be trained fairly +easily for a large number of scripts. In contrast to other system requiring +segmentation down to glyph level before classification, it is uniquely suited +for the recognition of connected scripts, because the neural network is trained +to assign correct character to unsegmented training data. + +Training a new model for kraken requires a variable amount of training data +manually generated from page images which have to be typographically similar to +the target prints that are to be recognized. As the system works on unsegmented +inputs for both training and recognition and its base unit is a text line, +training data are just transcriptions aligned to line images. + +Installing kraken +----------------- + +The easiest way to install and use kraken is through `conda +`_. kraken works both on Linux and Mac OS +X. After installing conda, download the environment file and create the +environment for kraken: + +.. code-block:: console + + $ wget https://raw.githubusercontent.com/mittagessen/kraken/master/environment.yml + $ conda env create -f environment.yml + +Each time you want to use the kraken environment in a shell is has to be +activated first: + +.. code-block:: console + + $ conda activate kraken + +Image acquisition and preprocessing +----------------------------------- + +First a number of high quality scans, preferably color or grayscale and at +least 300dpi are required. Scans should be in a lossless image format such as +TIFF or PNG, images in PDF files have to be extracted beforehand using a tool +such as ``pdftocairo`` or ``pdfimages``. While each of these requirements can +be relaxed to a degree, the final accuracy will suffer to some extent. For +example, only slightly compressed JPEG scans are generally suitable for +training and recognition. + +Depending on the source of the scans some preprocessing such as splitting scans +into pages, correcting skew and warp, and removing speckles is usually +required. For complex layouts such as newspapers it is advisable to split the +page manually into columns as the line extraction algorithm run to create +transcription environments does not deal well with non-codex page layouts. A +fairly user-friendly software for semi-automatic batch processing of image +scans is `Scantailor `_ albeit most work can be done +using a standard image editor. + +The total number of scans required depends on the nature of the script to be +recognized. Only features that are found on the page images and training data +derived from it can later be recognized, so it is important that the coverage +of typographic features is exhaustive. Training a single script model for a +fairly small script such as Arabic or Hebrew requires at least 800 lines, while +multi-script models, e.g. combined polytonic Greek and Latin, will require +significantly more transcriptions. + +There is no hard rule for the amount of training data and it may be required to +retrain a model after the initial training data proves insufficient. Most +``western`` texts contain between 25 and 40 lines per page, therefore upward of +30 pages have to be preprocessed and later transcribed. + +Transcription +------------- + +Transcription is done through local browser based HTML transcription +environments. These are created by the ``ketos transcribe`` command line util +that is part of kraken. Its basic input is just a number of image files and an +output path to write the HTML file to: + +.. code-block:: console + + $ ketos transcribe -o output.html image_1.png image_2.png ... + +While it is possible to put multiple images into a single transcription +environment splitting into one-image-per-HTML will ease parallel transcription +by multiple people. + +The above command reads in the image files, converts them to black and white if +necessary, tries to split them into line images, and puts an editable text +field next to the image in the HTML. + +Transcription has to be diplomatic, i.e. contain the exact character sequence +in the line image, including original orthography. Some deviations, such as +consistently omitting vocalization in Arabic texts, is possible as long as they +are systematic and relatively minor. + +.. note:: + + The page segmentation algorithm extracting lines from images is + optimized for ``western`` page layouts and may recognize lines + erroneously, lumping multiple lines together or cutting them in half. + The most efficient way to deal with these errors is just skipping the + affected lines by leaving the text box empty. + +.. tip:: + + Copy-paste transcription can significantly speed up the whole process. + Either transcribe scans of a work where a digital edition already + exists (but does not for typographically similar prints) or find a + sufficiently similar edition as a base. + +After transcribing a number of lines the results have to be saved, either using +the ``Download`` button on the lower left or through the regular ``Save Page +As`` (CTRL+S) function of the browser. All the work done is contained directly +in the saved files and it is possible to save partially transcribed files and +continue work later. + +Next the contents of the filled transcription environments have to be +extracted through the ``ketos extract`` command: + +.. code-block:: console + + $ ketos extract --output output_directory --normalization NFD *.html + +with + +--output + The output directory where all line image-text pairs (training data) + are written, defaulting to ``training/`` +--normalization + Unicode has code points to encode most glyphs encountered in the wild. + A lesser known feature is that there usually are multiple ways to + encode a glyph. `Unicode normalization + `_ ensures that equal glyphs are + encoded in the same way, i.e. that the encoded representation across + the training data set is consistent and there is only one way the + network can recognize a particular feature on the page. Usually it is + sufficient to set the normalization to Normalization Form + Decomposed (NFD), as it reduces the the size of the overall script to + be recognized slightly. + +The result will be a directory filled with line image text pairs ``NNNNNN.png`` +and ``NNNNNN.gt.txt`` and a ``manifest.txt`` containing a list of all extracted +lines. + +.. note:: + + At this point it is recommended to review the content of the training + data directory before proceeding. + +Training +-------- + +The training data in ``output_dir`` may now be used to train a new model by +invoking the ``ketos train`` command. Just hand a list of images to the command +such as: + +.. code-block:: console + + $ ketos train output_dir/*.png + +to start training. + +A number of lines will be split off into a separate held-out set that is used +to estimate the actual recognition accuracy achieved in the real world. These +are never shown to the network during training but will be recognized +periodically to evaluate the accuracy of the model. Per default the validation +set will comprise of 10% of the training data. + +Basic model training is mostly automatic albeit there are multiple parameters +that can be adjusted: + +--output + Sets the prefix for models generated during training. They will best as + ``prefix_epochs.mlmodel``. +--report + How often evaluation passes are run on the validation set. It is an + integer equal or larger than 1 with 1 meaning a report is created each + time the complete training set has been seen by the network. +--savefreq + How often intermediate models are saved to disk. It is an integer with + the same semantics as ``--report``. +--load + Continuing training is possible by loading an existing model file with + ``--load``. To continue training from a base model with another + training set refer to the full :ref:`ketos ` documentation. +--preload + Enables/disables preloading of the training set into memory for + accelerated training. The default setting preloads data sets with less + than 2500 lines, explicitly adding ``--preload`` will preload arbitrary + sized sets. ``--no-preload`` disables preloading in all circumstances. + +Training a network will take some time on a modern computer, even with the +default parameters. While the exact time required is unpredictable as training +is a somewhat random process a rough guide is that accuracy seldomly improves +after 50 epochs reached between 8 and 24 hours of training. + +When to stop training is a matter of experience; the default setting employs a +fairly reliable approach known as `early stopping +`_ that stops training as soon as +the error rate on the validation set doesn't improve anymore. This will +prevent `overfitting `_, i.e. +fitting the model to recognize only the training data properly instead of the +general patterns contained therein. + +.. code-block:: console + + $ ketos train output_dir/*.png + Building training set [####################################] 100% + Building validation set [####################################] 100% + [270.2364] alphabet mismatch {'9', '8', '݂', '3', '݀', '4', '1', '7', '5', '\xa0'} + Initializing model ✓ + Accuracy report (0) -1.5951 3680 9550 + epoch 0/-1 [####################################] 788/788 + Accuracy report (1) 0.0245 3504 3418 + epoch 1/-1 [####################################] 788/788 + Accuracy report (2) 0.8445 3504 545 + epoch 2/-1 [####################################] 788/788 + Accuracy report (3) 0.9541 3504 161 + epoch 3/-1 [------------------------------------] 13/788 0d 00:22:09 + ... + +By now there should be a couple of models model_name-1.mlmodel, +model_name-2.mlmodel, ... in the directory the script was executed in. Lets +take a look at each part of the output. + +.. code-block:: console + + Building training set [####################################] 100% + Building validation set [####################################] 100% + +shows the progress of loading the training and validation set into memory. This +might take a while as preprocessing the whole set and putting it into memory is +computationally intensive. Loading can be made faster without preloading at the +cost of performing preprocessing repeatedlyduring the training process. + +.. code-block:: console + + [270.2364] alphabet mismatch {'9', '8', '݂', '3', '݀', '4', '1', '7', '5', '\xa0'} + +is a warning about missing characters in either the validation or training set, +i.e. that the alphabets of the sets are not equal. Increasing the size of the +validation set will often remedy this warning. + +.. code-block:: console + + Accuracy report (2) 0.8445 3504 545 + +this line shows the results of the validation set evaluation. The error after 2 +epochs is 545 incorrect characters out of 3504 characters in the validation set +for a character accuracy of 84.4%. It should decrease fairly rapidly. If +accuracy remains around 0.30 something is amiss, e.g. non-reordered +right-to-left or wildly incorrect transcriptions. Abort training, correct the +error(s) and start again. + +After training is finished the best model is saved as +``model_name_best.mlmodel``. It is highly recommended to also archive the +training log and data for later reference. + +``ketos`` can also produce more verbose output with training set and network +information by appending one or more ``-v`` to the command: + +.. code-block:: console + + $ ketos -vv train syr/*.png + [0.7272] Building ground truth set from 876 line images + [0.7281] Taking 88 lines from training for evaluation + ... + [0.8479] Training set 788 lines, validation set 88 lines, alphabet 48 symbols + [0.8481] alphabet mismatch {'\xa0', '0', ':', '݀', '܇', '݂', '5'} + [0.8482] grapheme count + [0.8484] SPACE 5258 + [0.8484] ܐ 3519 + [0.8485] ܘ 2334 + [0.8486] ܝ 2096 + [0.8487] ܠ 1754 + [0.8487] ܢ 1724 + [0.8488] ܕ 1697 + [0.8489] ܗ 1681 + [0.8489] ܡ 1623 + [0.8490] ܪ 1359 + [0.8491] ܬ 1339 + [0.8491] ܒ 1184 + [0.8492] ܥ 824 + [0.8492] . 811 + [0.8493] COMBINING DOT BELOW 646 + [0.8493] ܟ 599 + [0.8494] ܫ 577 + [0.8495] COMBINING DIAERESIS 488 + [0.8495] ܚ 431 + [0.8496] ܦ 428 + [0.8496] ܩ 307 + [0.8497] COMBINING DOT ABOVE 259 + [0.8497] ܣ 256 + [0.8498] ܛ 204 + [0.8498] ܓ 176 + [0.8499] ܀ 132 + [0.8499] ܙ 81 + [0.8500] * 66 + [0.8501] ܨ 59 + [0.8501] ܆ 40 + [0.8502] [ 40 + [0.8503] ] 40 + [0.8503] 1 18 + [0.8504] 2 11 + [0.8504] ܇ 9 + [0.8505] 3 8 + [0.8505] 6 + [0.8506] 5 5 + [0.8506] NO-BREAK SPACE 4 + [0.8507] 0 4 + [0.8507] 6 4 + [0.8508] : 4 + [0.8508] 8 4 + [0.8509] 9 3 + [0.8510] 7 3 + [0.8510] 4 3 + [0.8511] SYRIAC FEMININE DOT 1 + [0.8511] SYRIAC RUKKAKHA 1 + [0.8512] Encoding training set + [0.9315] Creating new model [1,1,0,48 Lbx100 Do] with 49 outputs + [0.9318] layer type params + [0.9350] 0 rnn direction b transposed False summarize False out 100 legacy None + [0.9361] 1 dropout probability 0.5 dims 1 + [0.9381] 2 linear augmented False out 49 + [0.9918] Constructing RMSprop optimizer (lr: 0.001, momentum: 0.9) + [0.9920] Set OpenMP threads to 4 + [0.9920] Moving model to device cpu + [0.9924] Starting evaluation run + + +indicates that the training is running on 788 transcribed lines and a +validation set of 88 lines. 49 different classes, i.e. Unicode code points, +where found in these 788 lines. These affect the output size of the network; +obviously only these 49 different classes/code points can later be output by +the network. Importantly, we can see that certain characters occur markedly +less often than others. Characters like the Syriac feminine dot and numerals +that occur less than 10 times will most likely not be recognized well by the +trained net. + + +Evaluation and Validation +------------------------- + +While output during training is detailed enough to know when to stop training +one usually wants to know the specific kinds of errors to expect. Doing more +in-depth error analysis also allows to pinpoint weaknesses in the training +data, e.g. above average error rates for numerals indicate either a lack of +representation of numerals in the training data or erroneous transcription in +the first place. + +First the trained model has to be applied to some line transcriptions with the +`ketos test` command: + +.. code-block:: console + + $ ketos test -m syriac_best.mlmodel lines/*.png + Loading model syriac_best.mlmodel ✓ + Evaluating syriac_best.mlmodel + Evaluating [#-----------------------------------] 3% 00:04:56 + ... + +After all lines have been processed a evaluation report will be printed: + +.. code-block:: console + + === report === + + 35619 Characters + 336 Errors + 99.06% Accuracy + + 157 Insertions + 81 Deletions + 98 Substitutions + + Count Missed %Right + 27046 143 99.47% Syriac + 7015 52 99.26% Common + 1558 60 96.15% Inherited + + Errors Correct-Generated + 25 { } - { COMBINING DOT BELOW } + 25 { COMBINING DOT BELOW } - { } + 15 { . } - { } + 15 { COMBINING DIAERESIS } - { } + 12 { ܢ } - { } + 10 { } - { . } + 8 { COMBINING DOT ABOVE } - { } + 8 { ܝ } - { } + 7 { ZERO WIDTH NO-BREAK SPACE } - { } + 7 { ܆ } - { } + 7 { SPACE } - { } + 7 { ܣ } - { } + 6 { } - { ܝ } + 6 { COMBINING DOT ABOVE } - { COMBINING DIAERESIS } + 5 { ܙ } - { } + 5 { ܬ } - { } + 5 { } - { ܢ } + 4 { NO-BREAK SPACE } - { } + 4 { COMBINING DIAERESIS } - { COMBINING DOT ABOVE } + 4 { } - { ܒ } + 4 { } - { COMBINING DIAERESIS } + 4 { ܗ } - { } + 4 { } - { ܬ } + 4 { } - { ܘ } + 4 { ܕ } - { ܢ } + 3 { } - { ܕ } + 3 { ܐ } - { } + 3 { ܗ } - { ܐ } + 3 { ܝ } - { ܢ } + 3 { ܀ } - { . } + 3 { } - { ܗ } + + ..... + +The first section of the report consists of a simple accounting of the number +of characters in the ground truth, the errors in the recognition output and the +resulting accuracy in per cent. + +The next table lists the number of insertions (characters occuring in the +ground truth but not in the recognition output), substitutions (misrecognized +characters), and deletions (superfluous characters recognized by the model). + +Next is a grouping of errors (insertions and substitutions) by Unicode script. + +The final part of the report are errors sorted by frequency and a per +character accuracy report. Importantly most errors are incorrect recognition of +combining marks such as dots and diaereses. These may have several sources: +different dot placement in training and validation set, incorrect transcription +such as non-systematic transcription, or unclean speckled scans. Depending on +the error source, correction most often involves adding more training data and +fixing transcriptions. Sometimes it may even be advisable to remove +unrepresentative data from the training set. + +Recognition +----------- + +The ``kraken`` utility is employed for all non-training related tasks. Optical +character recognition is a multi-step process consisting of binarization +(conversion of input images to black and white), page segmentation (extracting +lines from the image), and recognition (converting line image to character +sequences). All of these may be run in a single call like this: + +.. code-block:: console + + $ kraken -i INPUT_IMAGE OUTPUT_FILE binarize segment ocr -m MODEL_FILE + +producing a text file from the input image. There are also `hocr +`_ and `ALTO `_ output +formats available through the appropriate switches: + +.. code-block:: console + + $ kraken -i ... ocr -h + $ kraken -i ... ocr -a + +For debugging purposes it is sometimes helpful to run each step manually and +inspect intermediate results: + +.. code-block:: console + + $ kraken -i INPUT_IMAGE BW_IMAGE binarize + $ kraken -i BW_IMAGE LINES segment + $ kraken -i BW_IMAGE OUTPUT_FILE ocr -l LINES ... + +It is also possible to recognize more than one file at a time by just chaining +``-i ... ...`` clauses like this: + +.. code-block:: console + + $ kraken -i input_1 output_1 -i input_2 output_2 ... + +Finally, there is an central repository containing freely available models. +Getting a list of all available models: + +.. code-block:: console + + $ kraken list + +Retrieving model metadata for a particular model: + +.. code-block:: console + + $ kraken show arabic-alam-al-kutub + name: arabic-alam-al-kutub.mlmodel + + An experimental model for Classical Arabic texts. + + Network trained on 889 lines of [0] as a test case for a general Classical + Arabic model. Ground truth was prepared by Sarah Savant + and Maxim Romanov . + + Vocalization was omitted in the ground truth. Training was stopped at ~35000 + iterations with an accuracy of 97%. + + [0] Ibn al-Faqīh (d. 365 AH). Kitāb al-buldān. Edited by Yūsuf al-Hādī, 1st + edition. Bayrūt: ʿĀlam al-kutub, 1416 AH/1996 CE. + alphabet: !()-.0123456789:[] «»،؟ءابةتثجحخدذرزسشصضطظعغفقكلمنهوىي ARABIC + MADDAH ABOVE, ARABIC HAMZA ABOVE, ARABIC HAMZA BELOW + +and actually fetching the model: + +.. code-block:: console + + $ kraken get arabic-alam-al-kutub + +The downloaded model can then be used for recognition by the name shown in its metadata, e.g.: + +.. code-block:: console + + $ kraken -i INPUT_IMAGE OUTPUT_FILE binarize segment ocr -m arabic-alam-al-kutub.mlmodel + +For more documentation see the kraken `website `_. diff --git a/2.0.0/_sources/vgsl.rst.txt b/2.0.0/_sources/vgsl.rst.txt new file mode 100644 index 000000000..c3e475d30 --- /dev/null +++ b/2.0.0/_sources/vgsl.rst.txt @@ -0,0 +1,185 @@ +.. _vgsl: + +VGSL network specification +========================== + +kraken implements a dialect of the Variable-size Graph Specification Language +(VGSL), enabling the specification of different network architectures for image +processing purposes using a short definition string. + +Basics +------ + +A VGSL specification consists of an input block, one or more layers, and an +output block. For example: + +.. code-block:: console + + [1,48,0,1 Cr3,3,32 Mp2,2 Cr3,3,64 Mp2,2 S1(1x12)1,3 Lbx100 Do O1c103] + +The first block defines the input in order of [batch, heigh, width, channels] +with zero-valued dimensions being variable. Integer valued height or width +input specifications will result in the input images being automatically scaled +in either dimension. + +When channels are set to 1 grayscale or B/W inputs are expected, 3 expects RGB +color images. Higher values in combination with a height of 1 result in the +network being fed 1 pixel wide grayscale strips scaled to the size of the +channel dimension. + +After the input, a number of layers are defined. Layers operate on the channel +dimension; this is intuitive for convolutional layers but a recurrent layer +doing sequence classification along the width axis on an image of a particular +height requires the height dimension to be moved to the channel dimension, +e.g.: + +.. code-block:: console + + [1,48,0,1 S1(1x48)1,3 Lbx100 O1c103] + +or using the alternative slightly faster formulation: + +.. code-block:: console + + [1,1,0,48 Lbx100 O1c103] + +Finally an output definition is appended. When training sequence classification +networks with the provided tools the appropriate output definition is +automatically appended to the network based on the alphabet of the training +data. + +Examples +-------- + +.. code-block:: console + + [1,1,0,48 Lbx100 Do 01c59] + + Creating new model [1,1,0,48 Lbx100 Do] with 59 outputs + layer type params + 0 rnn direction b transposed False summarize False out 100 legacy None + 1 dropout probability 0.5 dims 1 + 2 linear augmented False out 59 + +A simple recurrent recognition model with a single LSTM layer classifying lines +normalized to 48 pixels in height. + +.. code-block:: console + + [1,48,0,1 Cr3,3,32 Do0.1,2 Mp2,2 Cr3,3,64 Do0.1,2 Mp2,2 S1(1x12)1,3 Lbx100 Do 01c59] + + Creating new model [1,48,0,1 Cr3,3,32 Do0.1,2 Mp2,2 Cr3,3,64 Do0.1,2 Mp2,2 S1(1x12)1,3 Lbx100 Do] with 59 outputs + layer type params + 0 conv kernel 3 x 3 filters 32 activation r + 1 dropout probability 0.1 dims 2 + 2 maxpool kernel 2 x 2 stride 2 x 2 + 3 conv kernel 3 x 3 filters 64 activation r + 4 dropout probability 0.1 dims 2 + 5 maxpool kernel 2 x 2 stride 2 x 2 + 6 reshape from 1 1 x 12 to 1/3 + 7 rnn direction b transposed False summarize False out 100 legacy None + 8 dropout probability 0.5 dims 1 + 9 linear augmented False out 59 + +A model with a small convolutional stack before a recurrent LSTM layer. The +extended dropout layer syntax is used to reduce drop probability on the depth +dimension as the default is too high for convolutional layers. The remainder of +the height dimension (`12`) is reshaped into the depth dimensions before +applying the final recurrent and linear layers. + +.. code-block:: console + + [1,0,0,3 Cr3,3,16 Mp3,3 Lfys64 Lbx128 Lbx256 Do 01c59] + + Creating new model [1,0,0,3 Cr3,3,16 Mp3,3 Lfys64 Lbx128 Lbx256 Do] with 59 outputs + layer type params + 0 conv kernel 3 x 3 filters 16 activation r + 1 maxpool kernel 3 x 3 stride 3 x 3 + 2 rnn direction f transposed True summarize True out 64 legacy None + 3 rnn direction b transposed False summarize False out 128 legacy None + 4 rnn direction b transposed False summarize False out 256 legacy None + 5 dropout probability 0.5 dims 1 + 6 linear augmented False out 59 + +A model with arbitrary sized color image input, an initial summarizing +recurrent layer to squash the height to 64, followed by 2 bi-directional +recurrent layers and a linear projection. + +Convolutional Layers +-------------------- + +.. code-block:: console + + C[{name}](s|t|r|l|m)[{name}],, + s = sigmoid + t = tanh + r = relu + l = linear + m = softmax + +Adds a 2D convolution with kernel size `(y, x)` and `d` output channels, applying +the selected nonlinearity. + +Recurrent Layers +---------------- + +.. code-block:: console + + L[{name}](f|r|b)(x|y)[s][{name}] LSTM cell with n outputs. + G[{name}](f|r|b)(x|y)[s][{name}] GRU cell with n outputs. + f runs the RNN forward only. + r runs the RNN reversed only. + b runs the RNN bidirectionally. + s (optional) summarizes the output in the requested dimension, return the last step. + +Adds either an LSTM or GRU recurrent layer to the network using eiter the `x` +(width) or `y` (height) dimension as the time axis. Input features are the +channel dimension and the non-time-axis dimension (height/width) is treated as +another batch dimension. For example, a `Lfx25` layer on an `1, 16, 906, 32` +input will execute 16 independent forward passes on `906x32` tensors resulting +in an output of shape `1, 16, 906, 25`. If this isn't desired either run a +summarizing layer in the other direction, e.g. `Lfys20` for an input `1, 1, +906, 20`, or prepend a reshape layer `S1(1x16)1,3` combining the height and +channel dimension for an `1, 1, 906, 512` input to the recurrent layer. + +Helper and Plumbing Layers +-------------------------- + +Max Pool +^^^^^^^^ +.. code-block:: console + + Mp[{name}],[,,] + +Adds a maximum pooling with `(y, x)` kernel_size and `(y_stride, x_stride)` stride. + +Reshape +^^^^^^^ + +.. code-block:: console + + S[{name}](x), Splits one dimension, moves one part to another + dimension. + +The `S` layer reshapes a source dimension `d` to `a,b` and distributes `a` into +dimension `e`, respectively `b` into `f`. Either `e` or `f` has to be equal to +`d`. So `S1(1, 48)1, 3` on an `1, 48, 1020, 8` input will first reshape into +`1, 1, 48, 1020, 8`, leave the `1` part in the height dimension and distribute +the `48` sized tensor into the channel dimension resulting in a `1, 1, 1024, +48*8=384` sized output. `S` layers are mostly used to remove undesirable non-1 +height before a recurrent layer. + +.. note:: + + This `S` layer is equivalent to the one implemented in the tensorflow + implementation of VGSL, i.e. behaves differently from tesseract. + +Regularization Layers +--------------------- + +.. code-block:: console + + Do[{name}][],[] Insert a 1D or 2D dropout layer + +Adds an 1D or 2D dropout layer with a given probability. Defaults to `0.5` drop +probability and 1D dropout. Set to `dim` to `2` after convolutional layers. diff --git a/2.0.0/_static/alabaster.css b/2.0.0/_static/alabaster.css new file mode 100644 index 000000000..e3174bf93 --- /dev/null +++ b/2.0.0/_static/alabaster.css @@ -0,0 +1,708 @@ +@import url("basic.css"); + +/* -- page layout ----------------------------------------------------------- */ + +body { + font-family: Georgia, serif; + font-size: 17px; + background-color: #fff; + color: #000; + margin: 0; + padding: 0; +} + + +div.document { + width: 940px; + margin: 30px auto 0 auto; +} + +div.documentwrapper { + float: left; + width: 100%; +} + +div.bodywrapper { + margin: 0 0 0 220px; +} + +div.sphinxsidebar { + width: 220px; + font-size: 14px; + line-height: 1.5; +} + +hr { + border: 1px solid #B1B4B6; +} + +div.body { + background-color: #fff; + color: #3E4349; + padding: 0 30px 0 30px; +} + +div.body > .section { + text-align: left; +} + +div.footer { + width: 940px; + margin: 20px auto 30px auto; + font-size: 14px; + color: #888; + text-align: right; +} + +div.footer a { + color: #888; +} + +p.caption { + font-family: inherit; + font-size: inherit; +} + + +div.relations { + display: none; +} + + +div.sphinxsidebar { + max-height: 100%; + overflow-y: auto; +} + +div.sphinxsidebar a { + color: #444; + text-decoration: none; + border-bottom: 1px dotted #999; +} + +div.sphinxsidebar a:hover { + border-bottom: 1px solid #999; +} + +div.sphinxsidebarwrapper { + padding: 18px 10px; +} + +div.sphinxsidebarwrapper p.logo { + padding: 0; + margin: -10px 0 0 0px; + text-align: center; +} + +div.sphinxsidebarwrapper h1.logo { + margin-top: -10px; + text-align: center; + margin-bottom: 5px; + text-align: left; +} + +div.sphinxsidebarwrapper h1.logo-name { + margin-top: 0px; +} + +div.sphinxsidebarwrapper p.blurb { + margin-top: 0; + font-style: normal; +} + +div.sphinxsidebar h3, +div.sphinxsidebar h4 { + font-family: Georgia, serif; + color: #444; + font-size: 24px; + font-weight: normal; + margin: 0 0 5px 0; + padding: 0; +} + +div.sphinxsidebar h4 { + font-size: 20px; +} + +div.sphinxsidebar h3 a { + color: #444; +} + +div.sphinxsidebar p.logo a, +div.sphinxsidebar h3 a, +div.sphinxsidebar p.logo a:hover, +div.sphinxsidebar h3 a:hover { + border: none; +} + +div.sphinxsidebar p { + color: #555; + margin: 10px 0; +} + +div.sphinxsidebar ul { + margin: 10px 0; + padding: 0; + color: #000; +} + +div.sphinxsidebar ul li.toctree-l1 > a { + font-size: 120%; +} + +div.sphinxsidebar ul li.toctree-l2 > a { + font-size: 110%; +} + +div.sphinxsidebar input { + border: 1px solid #CCC; + font-family: Georgia, serif; + font-size: 1em; +} + +div.sphinxsidebar #searchbox input[type="text"] { + width: 160px; +} + +div.sphinxsidebar .search > div { + display: table-cell; +} + +div.sphinxsidebar hr { + border: none; + height: 1px; + color: #AAA; + background: #AAA; + + text-align: left; + margin-left: 0; + width: 50%; +} + +div.sphinxsidebar .badge { + border-bottom: none; +} + +div.sphinxsidebar .badge:hover { + border-bottom: none; +} + +/* To address an issue with donation coming after search */ +div.sphinxsidebar h3.donation { + margin-top: 10px; +} + +/* -- body styles ----------------------------------------------------------- */ + +a { + color: #004B6B; + text-decoration: underline; +} + +a:hover { + color: #6D4100; + text-decoration: underline; +} + +div.body h1, +div.body h2, +div.body h3, +div.body h4, +div.body h5, +div.body h6 { + font-family: Georgia, serif; + font-weight: normal; + margin: 30px 0px 10px 0px; + padding: 0; +} + +div.body h1 { margin-top: 0; padding-top: 0; font-size: 240%; } +div.body h2 { font-size: 180%; } +div.body h3 { font-size: 150%; } +div.body h4 { font-size: 130%; } +div.body h5 { font-size: 100%; } +div.body h6 { font-size: 100%; } + +a.headerlink { + color: #DDD; + padding: 0 4px; + text-decoration: none; +} + +a.headerlink:hover { + color: #444; + background: #EAEAEA; +} + +div.body p, div.body dd, div.body li { + line-height: 1.4em; +} + +div.admonition { + margin: 20px 0px; + padding: 10px 30px; + background-color: #EEE; + border: 1px solid #CCC; +} + +div.admonition tt.xref, div.admonition code.xref, div.admonition a tt { + background-color: #FBFBFB; + border-bottom: 1px solid #fafafa; +} + +div.admonition p.admonition-title { + font-family: Georgia, serif; + font-weight: normal; + font-size: 24px; + margin: 0 0 10px 0; + padding: 0; + line-height: 1; +} + +div.admonition p.last { + margin-bottom: 0; +} + +div.highlight { + background-color: #fff; +} + +dt:target, .highlight { + background: #FAF3E8; +} + +div.warning { + background-color: #FCC; + border: 1px solid #FAA; +} + +div.danger { + background-color: #FCC; + border: 1px solid #FAA; + -moz-box-shadow: 2px 2px 4px #D52C2C; + -webkit-box-shadow: 2px 2px 4px #D52C2C; + box-shadow: 2px 2px 4px #D52C2C; +} + +div.error { + background-color: #FCC; + border: 1px solid #FAA; + -moz-box-shadow: 2px 2px 4px #D52C2C; + -webkit-box-shadow: 2px 2px 4px #D52C2C; + box-shadow: 2px 2px 4px #D52C2C; +} + +div.caution { + background-color: #FCC; + border: 1px solid #FAA; +} + +div.attention { + background-color: #FCC; + border: 1px solid #FAA; +} + +div.important { + background-color: #EEE; + border: 1px solid #CCC; +} + +div.note { + background-color: #EEE; + border: 1px solid #CCC; +} + +div.tip { + background-color: #EEE; + border: 1px solid #CCC; +} + +div.hint { + background-color: #EEE; + border: 1px solid #CCC; +} + +div.seealso { + background-color: #EEE; + border: 1px solid #CCC; +} + +div.topic { + background-color: #EEE; +} + +p.admonition-title { + display: inline; +} + +p.admonition-title:after { + content: ":"; +} + +pre, tt, code { + font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace; + font-size: 0.9em; +} + +.hll { + background-color: #FFC; + margin: 0 -12px; + padding: 0 12px; + display: block; +} + +img.screenshot { +} + +tt.descname, tt.descclassname, code.descname, code.descclassname { + font-size: 0.95em; +} + +tt.descname, code.descname { + padding-right: 0.08em; +} + +img.screenshot { + -moz-box-shadow: 2px 2px 4px #EEE; + -webkit-box-shadow: 2px 2px 4px #EEE; + box-shadow: 2px 2px 4px #EEE; +} + +table.docutils { + border: 1px solid #888; + -moz-box-shadow: 2px 2px 4px #EEE; + -webkit-box-shadow: 2px 2px 4px #EEE; + box-shadow: 2px 2px 4px #EEE; +} + +table.docutils td, table.docutils th { + border: 1px solid #888; + padding: 0.25em 0.7em; +} + +table.field-list, table.footnote { + border: none; + -moz-box-shadow: none; + -webkit-box-shadow: none; + box-shadow: none; +} + +table.footnote { + margin: 15px 0; + width: 100%; + border: 1px solid #EEE; + background: #FDFDFD; + font-size: 0.9em; +} + +table.footnote + table.footnote { + margin-top: -15px; + border-top: none; +} + +table.field-list th { + padding: 0 0.8em 0 0; +} + +table.field-list td { + padding: 0; +} + +table.field-list p { + margin-bottom: 0.8em; +} + +/* Cloned from + * https://github.com/sphinx-doc/sphinx/commit/ef60dbfce09286b20b7385333d63a60321784e68 + */ +.field-name { + -moz-hyphens: manual; + -ms-hyphens: manual; + -webkit-hyphens: manual; + hyphens: manual; +} + +table.footnote td.label { + width: .1px; + padding: 0.3em 0 0.3em 0.5em; +} + +table.footnote td { + padding: 0.3em 0.5em; +} + +dl { + margin-left: 0; + margin-right: 0; + margin-top: 0; + padding: 0; +} + +dl dd { + margin-left: 30px; +} + +blockquote { + margin: 0 0 0 30px; + padding: 0; +} + +ul, ol { + /* Matches the 30px from the narrow-screen "li > ul" selector below */ + margin: 10px 0 10px 30px; + padding: 0; +} + +pre { + background: #EEE; + padding: 7px 30px; + margin: 15px 0px; + line-height: 1.3em; +} + +div.viewcode-block:target { + background: #ffd; +} + +dl pre, blockquote pre, li pre { + margin-left: 0; + padding-left: 30px; +} + +tt, code { + background-color: #ecf0f3; + color: #222; + /* padding: 1px 2px; */ +} + +tt.xref, code.xref, a tt { + background-color: #FBFBFB; + border-bottom: 1px solid #fff; +} + +a.reference { + text-decoration: none; + border-bottom: 1px dotted #004B6B; +} + +/* Don't put an underline on images */ +a.image-reference, a.image-reference:hover { + border-bottom: none; +} + +a.reference:hover { + border-bottom: 1px solid #6D4100; +} + +a.footnote-reference { + text-decoration: none; + font-size: 0.7em; + vertical-align: top; + border-bottom: 1px dotted #004B6B; +} + +a.footnote-reference:hover { + border-bottom: 1px solid #6D4100; +} + +a:hover tt, a:hover code { + background: #EEE; +} + + +@media screen and (max-width: 870px) { + + div.sphinxsidebar { + display: none; + } + + div.document { + width: 100%; + + } + + div.documentwrapper { + margin-left: 0; + margin-top: 0; + margin-right: 0; + margin-bottom: 0; + } + + div.bodywrapper { + margin-top: 0; + margin-right: 0; + margin-bottom: 0; + margin-left: 0; + } + + ul { + margin-left: 0; + } + + li > ul { + /* Matches the 30px from the "ul, ol" selector above */ + margin-left: 30px; + } + + .document { + width: auto; + } + + .footer { + width: auto; + } + + .bodywrapper { + margin: 0; + } + + .footer { + width: auto; + } + + .github { + display: none; + } + + + +} + + + +@media screen and (max-width: 875px) { + + body { + margin: 0; + padding: 20px 30px; + } + + div.documentwrapper { + float: none; + background: #fff; + } + + div.sphinxsidebar { + display: block; + float: none; + width: 102.5%; + margin: 50px -30px -20px -30px; + padding: 10px 20px; + background: #333; + color: #FFF; + } + + div.sphinxsidebar h3, div.sphinxsidebar h4, div.sphinxsidebar p, + div.sphinxsidebar h3 a { + color: #fff; + } + + div.sphinxsidebar a { + color: #AAA; + } + + div.sphinxsidebar p.logo { + display: none; + } + + div.document { + width: 100%; + margin: 0; + } + + div.footer { + display: none; + } + + div.bodywrapper { + margin: 0; + } + + div.body { + min-height: 0; + padding: 0; + } + + .rtd_doc_footer { + display: none; + } + + .document { + width: auto; + } + + .footer { + width: auto; + } + + .footer { + width: auto; + } + + .github { + display: none; + } +} + + +/* misc. */ + +.revsys-inline { + display: none!important; +} + +/* Hide ugly table cell borders in ..bibliography:: directive output */ +table.docutils.citation, table.docutils.citation td, table.docutils.citation th { + border: none; + /* Below needed in some edge cases; if not applied, bottom shadows appear */ + -moz-box-shadow: none; + -webkit-box-shadow: none; + box-shadow: none; +} + + +/* relbar */ + +.related { + line-height: 30px; + width: 100%; + font-size: 0.9rem; +} + +.related.top { + border-bottom: 1px solid #EEE; + margin-bottom: 20px; +} + +.related.bottom { + border-top: 1px solid #EEE; +} + +.related ul { + padding: 0; + margin: 0; + list-style: none; +} + +.related li { + display: inline; +} + +nav#rellinks { + float: right; +} + +nav#rellinks li+li:before { + content: "|"; +} + +nav#breadcrumbs li+li:before { + content: "\00BB"; +} + +/* Hide certain items when printing */ +@media print { + div.related { + display: none; + } +} \ No newline at end of file diff --git a/2.0.0/_static/basic.css b/2.0.0/_static/basic.css new file mode 100644 index 000000000..e5179b7a9 --- /dev/null +++ b/2.0.0/_static/basic.css @@ -0,0 +1,925 @@ +/* + * basic.css + * ~~~~~~~~~ + * + * Sphinx stylesheet -- basic theme. + * + * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ + +/* -- main layout ----------------------------------------------------------- */ + +div.clearer { + clear: both; +} + +div.section::after { + display: block; + content: ''; + clear: left; +} + +/* -- relbar ---------------------------------------------------------------- */ + +div.related { + width: 100%; + font-size: 90%; +} + +div.related h3 { + display: none; +} + +div.related ul { + margin: 0; + padding: 0 0 0 10px; + list-style: none; +} + +div.related li { + display: inline; +} + +div.related li.right { + float: right; + margin-right: 5px; +} + +/* -- sidebar --------------------------------------------------------------- */ + +div.sphinxsidebarwrapper { + padding: 10px 5px 0 10px; +} + +div.sphinxsidebar { + float: left; + width: 230px; + margin-left: -100%; + font-size: 90%; + word-wrap: break-word; + overflow-wrap : break-word; +} + +div.sphinxsidebar ul { + list-style: none; +} + +div.sphinxsidebar ul ul, +div.sphinxsidebar ul.want-points { + margin-left: 20px; + list-style: square; +} + +div.sphinxsidebar ul ul { + margin-top: 0; + margin-bottom: 0; +} + +div.sphinxsidebar form { + margin-top: 10px; +} + +div.sphinxsidebar input { + border: 1px solid #98dbcc; + font-family: sans-serif; + font-size: 1em; +} + +div.sphinxsidebar #searchbox form.search { + overflow: hidden; +} + +div.sphinxsidebar #searchbox input[type="text"] { + float: left; + width: 80%; + padding: 0.25em; + box-sizing: border-box; +} + +div.sphinxsidebar #searchbox input[type="submit"] { + float: left; + width: 20%; + border-left: none; + padding: 0.25em; + box-sizing: border-box; +} + + +img { + border: 0; + max-width: 100%; +} + +/* -- search page ----------------------------------------------------------- */ + +ul.search { + margin: 10px 0 0 20px; + padding: 0; +} + +ul.search li { + padding: 5px 0 5px 20px; + background-image: url(file.png); + background-repeat: no-repeat; + background-position: 0 7px; +} + +ul.search li a { + font-weight: bold; +} + +ul.search li p.context { + color: #888; + margin: 2px 0 0 30px; + text-align: left; +} + +ul.keywordmatches li.goodmatch a { + font-weight: bold; +} + +/* -- index page ------------------------------------------------------------ */ + +table.contentstable { + width: 90%; + margin-left: auto; + margin-right: auto; +} + +table.contentstable p.biglink { + line-height: 150%; +} + +a.biglink { + font-size: 1.3em; +} + +span.linkdescr { + font-style: italic; + padding-top: 5px; + font-size: 90%; +} + +/* -- general index --------------------------------------------------------- */ + +table.indextable { + width: 100%; +} + +table.indextable td { + text-align: left; + vertical-align: top; +} + +table.indextable ul { + margin-top: 0; + margin-bottom: 0; + list-style-type: none; +} + +table.indextable > tbody > tr > td > ul { + padding-left: 0em; +} + +table.indextable tr.pcap { + height: 10px; +} + +table.indextable tr.cap { + margin-top: 10px; + background-color: #f2f2f2; +} + +img.toggler { + margin-right: 3px; + margin-top: 3px; + cursor: pointer; +} + +div.modindex-jumpbox { + border-top: 1px solid #ddd; + border-bottom: 1px solid #ddd; + margin: 1em 0 1em 0; + padding: 0.4em; +} + +div.genindex-jumpbox { + border-top: 1px solid #ddd; + border-bottom: 1px solid #ddd; + margin: 1em 0 1em 0; + padding: 0.4em; +} + +/* -- domain module index --------------------------------------------------- */ + +table.modindextable td { + padding: 2px; + border-collapse: collapse; +} + +/* -- general body styles --------------------------------------------------- */ + +div.body { + min-width: inherit; + max-width: 800px; +} + +div.body p, div.body dd, div.body li, div.body blockquote { + -moz-hyphens: auto; + -ms-hyphens: auto; + -webkit-hyphens: auto; + hyphens: auto; +} + +a.headerlink { + visibility: hidden; +} + +a:visited { + color: #551A8B; +} + +h1:hover > a.headerlink, +h2:hover > a.headerlink, +h3:hover > a.headerlink, +h4:hover > a.headerlink, +h5:hover > a.headerlink, +h6:hover > a.headerlink, +dt:hover > a.headerlink, +caption:hover > a.headerlink, +p.caption:hover > a.headerlink, +div.code-block-caption:hover > a.headerlink { + visibility: visible; +} + +div.body p.caption { + text-align: inherit; +} + +div.body td { + text-align: left; +} + +.first { + margin-top: 0 !important; +} + +p.rubric { + margin-top: 30px; + font-weight: bold; +} + +img.align-left, figure.align-left, .figure.align-left, object.align-left { + clear: left; + float: left; + margin-right: 1em; +} + +img.align-right, figure.align-right, .figure.align-right, object.align-right { + clear: right; + float: right; + margin-left: 1em; +} + +img.align-center, figure.align-center, .figure.align-center, object.align-center { + display: block; + margin-left: auto; + margin-right: auto; +} + +img.align-default, figure.align-default, .figure.align-default { + display: block; + margin-left: auto; + margin-right: auto; +} + +.align-left { + text-align: left; +} + +.align-center { + text-align: center; +} + +.align-default { + text-align: center; +} + +.align-right { + text-align: right; +} + +/* -- sidebars -------------------------------------------------------------- */ + +div.sidebar, +aside.sidebar { + margin: 0 0 0.5em 1em; + border: 1px solid #ddb; + padding: 7px; + background-color: #ffe; + width: 40%; 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+} + +th > :last-child, +td > :last-child { + margin-bottom: 0px; +} + +/* -- figures --------------------------------------------------------------- */ + +div.figure, figure { + margin: 0.5em; + padding: 0.5em; +} + +div.figure p.caption, figcaption { + padding: 0.3em; +} + +div.figure p.caption span.caption-number, +figcaption span.caption-number { + font-style: italic; +} + +div.figure p.caption span.caption-text, +figcaption span.caption-text { +} + +/* -- field list styles ----------------------------------------------------- */ + +table.field-list td, table.field-list th { + border: 0 !important; +} + +.field-list ul { + margin: 0; + padding-left: 1em; +} + +.field-list p { + margin: 0; +} + +.field-name { + -moz-hyphens: manual; + -ms-hyphens: manual; + -webkit-hyphens: manual; + hyphens: manual; +} + +/* -- hlist styles ---------------------------------------------------------- */ + +table.hlist { + margin: 1em 0; +} + +table.hlist td { + vertical-align: top; +} + +/* -- object description styles --------------------------------------------- */ + +.sig { + font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace; 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