kraken-didip 0.0.1.dev2116
OCR/HTR engine for all the languages: a fork for the DiDip project.
License: Apache
Requires Python: <=3.11.99,>=3.8
Keywords: ocr, htr

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. This fork is meant to support the EU-funded Digital-to-Distant Diplomatics project.

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

DiDip changes:

  - custom functions for containers
  - fixing dependencies (eg. python-bidi)
  - ...

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_didip

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
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          :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+).