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, BiDi, and Top-to-Bottom script support
• ALTO, PageXML, abbyyXML, and hOCR output
• Word bounding boxes and character cuts
• Multi-script recognition support
• Public repository of model files
• Variable recognition network architecture

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

The latest stable releases can be installed either from PyPi:

$ pip install kraken

or through conda:

$ 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

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.

Have a look at the docs.

These days kraken is quite closely linked to the 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 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.

kraken is developed at the École Pratique des Hautes Études, Université PSL.

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

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+).