Better examples in docs

mittagessen · Mar 27, 2024 · 6995394 · 6995394
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diff --git a/README.rst b/README.rst
@@ -9,7 +9,7 @@ material.
 
 kraken's main features are:
 
-  - Fully trainable layout analysis and character recognition
+  - Fully trainable layout analysis, reading order, and character recognition
   - `Right-to-Left <https://en.wikipedia.org/wiki/Right-to-left>`_, `BiDi
     <https://en.wikipedia.org/wiki/Bi-directional_text>`_, and Top-to-Bottom
     script support
@@ -44,14 +44,14 @@ install the `pdf` extras package for PyPi:
 
   $ pip install kraken[pdf]
 
-or install `pyvips` manually with conda:
+or install `pyvips` manually with pip:
 
 ::
 
-  $ conda install -c conda-forge pyvips
+  $ pip install pyvips
 
-Conda environment files are provided which for the seamless installation of the
-main branch as well:
+Conda environment files are provided for the seamless installation of the main
+branch as well:
 
 ::
 
@@ -70,12 +70,12 @@ or:
 for CUDA acceleration with the appropriate hardware.
 
 Finally you'll have to scrounge up a model to do the actual recognition of
-characters. To download the default model for printed English text and place it
+characters. To download the default model for printed French text and place it
 in the kraken directory for the current user:
 
 ::
 
-  $ kraken get 10.5281/zenodo.2577813 
+  $ kraken get 10.5281/zenodo.10592716 
 
 A list of libre models available in the central repository can be retrieved by
 running:
@@ -111,7 +111,7 @@ To segment and OCR an image using the default model(s):
 
 ::
 
-  $ kraken -i image.tif image.txt segment -bl ocr
+  $ kraken -i image.tif image.txt segment -bl ocr -m catmus-print-fondue-large.mlmodel
 
 All subcommands and options are documented. Use the ``help`` option to get more
 information.

diff --git a/docs/index.rst b/docs/index.rst
@@ -103,13 +103,14 @@ for CUDA acceleration with the appropriate hardware.
 Finding Recognition 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:
+Finally you'll have to scrounge up a model to do the actual recognition of
+characters. To download the default model for printed French text and place it
+in the kraken directory for the current user:
 
-.. code-block:: console
+::
+
+  $ kraken get 10.5281/zenodo.10592716 
 
-  $ kraken get 10.5281/zenodo.2577813
 
 A list of libre models available in the central repository can be retrieved by
 running:
@@ -122,18 +123,22 @@ Model metadata can be extracted using:
 
 .. code-block:: console
 
-  $ kraken show 10.5281/zenodo.2577813
-  name: 10.5281/zenodo.2577813
-
-  A generalized model for English printed text
+  $ kraken show 10.5281/zenodo.10592716
+  name: 10.5281/zenodo.10592716
+  
+  CATMuS-Print (Large, 2024-01-30) - Diachronic model for French prints and other languages
   
-  This model has been trained on a large corpus of modern printed English text\naugmented with ~10000 lines of historical p
+  <p><strong>CATMuS-Print (Large) - Diachronic model for French prints and other West European languages</strong></p>
+  <p>CATMuS (Consistent Approach to Transcribing ManuScript) Print is a Kraken HTR model trained on data produced by several projects, dealing with different languages (French, Spanish, German, English, Corsican, Catalan, Latin, Italian&hellip;) and different centuries (from the first prints of the 16th c. to digital documents of the 21st century).</p>
+  <p>Transcriptions follow graphematic principles and try to be as compatible as possible with guidelines previously published for French: no ligature (except those that still exist), no allographetic variants (except the long s), and preservation of the historical use of some letters (u/v, i/j). Abbreviations are not resolved. Inconsistencies might be present, because transcriptions have been done over several years and the norms have slightly evolved.</p>
+  <p>The model is trained with NFKD Unicode normalization: each diacritic (including superscripts) are transcribed as their own characters, separately from the "main" character.</p>
+  <p>This model is the result of the collaboration from researchers from the University of Geneva and Inria Paris and will be consolidated under the CATMuS Medieval Guidelines in an upcoming paper.</p>
   scripts: Latn
-  alphabet: !"#$%&'()+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[]`abcdefghijklmnopqrstuvwxyz{} SPACE
-  accuracy: 99.95%
-  license: Apache-2.0
-  author(s): Kiessling, Benjamin
-  date: 2019-02-26
+  alphabet: !"#$%&'()*+,-./0123456789:;<=>?ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_abcdefghijklmnopqrstuvwxyz|}~¡£¥§«¬°¶·»¿ÆßæđłŒœƀǝɇΑΒΓΔΕΖΘΙΚΛΜΝΟΠΡΣΤΥΦΧΩαβγδεζηθικλμνξοπρςστυφχωϛחלרᑕᗅᗞᚠẞ–—‘’‚“”„‟†•⁄⁊⁋℟←▽◊★☙✠✺✻⟦⟧⬪ꝑꝓꝗꝙꝟꝯꝵ SPACE, COMBINING GRAVE ACCENT, COMBINING ACUTE ACCENT, COMBINING CIRCUMFLEX ACCENT, COMBINING TILDE, COMBINING MACRON, COMBINING DOT ABOVE, COMBINING DIAERESIS, COMBINING RING ABOVE, COMBINING COMMA ABOVE, COMBINING REVERSED COMMA ABOVE, COMBINING CEDILLA, COMBINING OGONEK, COMBINING GREEK PERISPOMENI, COMBINING GREEK YPOGEGRAMMENI, COMBINING LATIN SMALL LETTER I, COMBINING LATIN SMALL LETTER U, 0xe682, 0xe68b, 0xe8bf, 0xf1a7
+  accuracy: 98.56%
+  license: cc-by-4.0
+  author(s): Gabay, Simon; Clérice, Thibault
+  date: 2024-01-30
 
 Quickstart
 ==========
@@ -154,7 +159,7 @@ prerequisite step of page segmentation:
 
 .. code-block:: console
 
-  $ kraken -i image.tif image.txt segment -bl ocr
+  $ kraken -i image.tif image.txt segment -bl ocr -m catmus-print-fondue-large.mlmodel
   Loading RNN     ✓
   Processing      ⣻
 
@@ -164,18 +169,18 @@ To segment an image into reading-order sorted baselines and regions:
 
   $ kraken -i bw.tif lines.json segment -bl
 
-To OCR an image using the default model:
+To OCR an image using the previously downloaded model:
 
 .. code-block:: console
 
-  $ kraken -i bw.tif image.txt segment -bl ocr
+  $ kraken -i bw.tif image.txt segment -bl ocr -m catmus-print-fondue-large.mlmodel
 
 To OCR an image using the default model and serialize the output using the ALTO
 template:
 
 .. code-block:: console
 
-  $ kraken -a -i bw.tif image.txt segment -bl ocr
+  $ kraken -a -i bw.tif image.txt segment -bl ocr -m catmus-print-fondue-large.mlmodel
 
 All commands and their parameters are documented, just add the standard
 ``--help`` flag for further information.