Description

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kraken is a turn-key OCR system optimized for historical and non-Latin script material.

kraken's main features are:

• Fully trainable layout analysis 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
• ALTO <https://www.loc.gov/standards/alto/>_, PageXML, abbyyXML, and hOCR output
• Word bounding boxes and character cuts
• Multi-script recognition support
• Public repository <https://zenodo.org/communities/ocr_models>_ of model files
• Lightweight model files
• Variable recognition network architectures

Installation

kraken only runs on Linux or Mac OS X. Windows is not supported.

The latest stable releases can be installed either from PyPi <https://pypi.org>_:

::

$ pip install kraken

or through conda <https://anaconda.org>_:

::

$ conda install -c conda-forge -c mittagessen kraken

If you want direct PDF and multi-image TIFF/JPEG2000 support it is necessary to install the pdf extras package for PyPi:

::

$ pip install kraken[pdf]

or install pyvips manually with conda:

::

$ conda install -c conda-forge pyvips

Conda environment files are provided which for the seamless installation of the master branch as well:

::

$ git clone https://github.com/mittagessen/kraken.git $ cd kraken $ conda env create -f environment.yml

or:

::

$ git clone https://github.com/mittagessen/kraken.git $ cd kraken $ conda env create -f environment_cuda.yml

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 in the kraken directory for the current user:

::

$ kraken get 10.5281/zenodo.2577813

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

::

$ kraken list

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

To binarize a single image using the nlbin algorithm:

::

$ kraken -i image.tif bw.png binarize

To segment an image (binarized or not) with the new baseline segmenter:

::

$ kraken -i image.tif lines.json segment -bl

To segment and OCR an image using the default model(s):

::

$ kraken -i image.tif image.txt segment -bl ocr

All subcommands and options are documented. Use the help option to get more information.

Documentation

Have a look at the docs <http://kraken.re>_

Related Software

These days kraken is quite closely linked to the escriptorium <https://escriptorium.fr>_ project developed in the same eScripta research group. eScriptorium provides a user-friendly interface for annotating data, training models, and inference (but also much more). There is a gitter channel <https://gitter.im/escripta/escriptorium>_ that is mostly intended for coordinating technical development but is also a spot to find people with experience on applying kraken on a wide variety of material.

Funding

kraken is developed at the École Pratique des Hautes Études <http://ephe.fr>, Université PSL <http://www.psl.eu>.

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    .. image:: https://ec.europa.eu/regional_policy/images/information/logos/eu_flag.jpg
      :width: 100
      :alt: Co-financed by the European Union

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    This project was partially funded through the RESILIENCE project, funded from
    the European Union’s Horizon 2020 Framework Programme for Research and
    Innovation.

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  .. image:: https://www.gouvernement.fr/sites/default/files/styles/illustration-centre/public/contenu/illustration/2018/10/logo_investirlavenir_rvb.png
     :width: 100
     :alt: Received funding from the Programme d’investissements d’Avenir

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    Ce travail a bénéficié d’une aide de l’État gérée par l’Agence Nationale de la
    Recherche au titre du Programme d’Investissements d’Avenir portant la référence
    ANR-21-ESRE-0005.