kraken 7.0.0b3
OCR/HTR engine for all the languages
Requires Python: <3.14,>=3.10
Keywords: htr, ocr

Description
===========

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    :target: https://github.com/mittagessen/kraken/actions/workflows/test.yml

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

kraken's main features are:

  - 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
  - `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
  - Variable recognition network architecture

Installation
============

Kraken can be run on Linux or Mac OS X (both x64 and ARM). Installation is
through the on-board *pip* utility. To not pollute the global state of your
distribution's package manager it is recommended to use virtual environments.
If you do not have a setup or do not wish to handle virtual environments
yourself you can use `pipx`.

.. code-block:: console

   $ sudo apt install pipx
   $ pipx install kraken

kraken works both on Linux and Mac OS X and with any python interpreter between
3.10 and 3.13. It is possible the installation fails because `pipx` defaults to
an unsupported interpreter version. In that case you need to install a
compatible interpreter version such as 3.11 and then specify this version
explicitly:

.. code-block:: console

   $ sudo apt install python3.13-full
   $ pipx install --python python3.13 kraken


Installation using pip
----------------------

Create and activate a separate virtual environment using whatever tool you
like.

.. code-block:: console

  $ pip install kraken

or by running pip in the git repository:

.. code-block:: console

  $ pip install .

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

.. code-block:: console

   $ pip install kraken[pdf]

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:

::

  $ kraken get 10.5281/zenodo.10592716

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 -m catmus-print-fondue-large.mlmodel

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

Documentation
=============

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

Related Software
================

These days kraken is quite closely linked to the `eScriptorium
<https://gitlab.com/scripta/escriptorium/>`_ 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 <https://www.ephe.psl.eu>`_, `Université PSL <https://www.psl.eu>`_.

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        .. image:: https://raw.githubusercontent.com/mittagessen/kraken/main/docs/_static/normal-reproduction-low-resolution.jpg
          :width: 100
          :alt: Co-financed by the European Union

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        This project was funded in part by the European Union. (ERC, MiDRASH,
        project number 101071829).

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        .. image:: https://raw.githubusercontent.com/mittagessen/kraken/main/docs/_static/normal-reproduction-low-resolution.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://projet.biblissima.fr/sites/default/files/2021-11/biblissima-baseline-sombre-ia.png
         :width: 400
         :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 (Biblissima+).