| Name | kraken JSON |
| Version |
5.2.2
JSON |
| download |
| home_page | https://kraken.re |
| Summary | OCR/HTR engine for all the languages |
| upload_time | 2024-04-30 13:23:47 |
| maintainer | None |
| docs_url | None |
| author | Benjamin Kiessling |
| requires_python | <=3.11.99,>=3.8 |
| license | Apache |
| keywords |
ocr
htr
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
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+).
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