Metadata-Version: 2.1
Name: kraken
Version: 5.2.8
Summary: OCR/HTR engine for all the languages
Home-page: https://kraken.re
Author: Benjamin Kiessling
Author-email: mittagessen@l.unchti.me
License: Apache
Keywords: ocr,htr
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Environment :: GPU
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: POSIX
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: <=3.11.99,>=3.8
Description-Content-Type: text/x-rst; charset=UTF-8
License-File: LICENSE
Requires-Dist: jsonschema
Requires-Dist: lxml
Requires-Dist: requests
Requires-Dist: click>=8.1
Requires-Dist: numpy~=1.23.0
Requires-Dist: Pillow>=9.2.0
Requires-Dist: regex
Requires-Dist: scipy~=1.10.0
Requires-Dist: protobuf>=3.0.0
Requires-Dist: coremltools~=6.0
Requires-Dist: jinja2~=3.0
Requires-Dist: python-bidi~=0.4.0
Requires-Dist: torchvision>=0.5.0
Requires-Dist: torch~=2.1.0
Requires-Dist: scikit-learn~=1.2.1
Requires-Dist: scikit-image~=0.21.0
Requires-Dist: shapely~=1.8.5
Requires-Dist: pyarrow
Requires-Dist: lightning~=2.2.0
Requires-Dist: torchmetrics>=1.1.0
Requires-Dist: threadpoolctl~=3.4.0
Requires-Dist: importlib-resources>=1.3.0
Requires-Dist: rich
Provides-Extra: test
Requires-Dist: hocr-spec; extra == "test"
Requires-Dist: pytest; extra == "test"
Provides-Extra: pdf
Requires-Dist: pyvips; extra == "pdf"
Provides-Extra: augment
Requires-Dist: albumentations; extra == "augment"

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

.. image:: https://github.com/mittagessen/kraken/actions/workflows/test.yml/badge.svg
    :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 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 pip:

::

  $ pip install pyvips

Conda environment files are provided for the seamless installation of the main
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 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>`_.

.. container:: twocol

   .. container::

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

   .. container::

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


.. container:: twocol

   .. container::

      .. 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

   .. container::

        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+).


