Skip to main content

OCR/HTR engine for all the languages

Project description

Description

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

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

kraken’s main features are:

  • Fully trainable layout analysis 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 architectures

Installation

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 conda:

$ conda install -c conda-forge pyvips

Conda environment files are provided which 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 English text and place it in the kraken directory for the current user:

$ kraken get 10.5281/zenodo.2577813

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

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

Documentation

Have a look at the docs

Funding

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

Co-financed by the European Union

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

Received funding from the Programme d’investissements d’Avenir

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+).

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

kraken-4.3.12.tar.gz (12.6 MB view details)

Uploaded Source

Built Distribution

kraken-4.3.12-py3-none-any.whl (5.0 MB view details)

Uploaded Python 3

File details

Details for the file kraken-4.3.12.tar.gz.

File metadata

  • Download URL: kraken-4.3.12.tar.gz
  • Upload date:
  • Size: 12.6 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.11.3

File hashes

Hashes for kraken-4.3.12.tar.gz
Algorithm Hash digest
SHA256 67a278bc645719a555713794cbf2a26b00107f426c342447a77a0742673b4c4c
MD5 de6312abf4dad6a1e00a305ee2ca3d4d
BLAKE2b-256 7f5d7e60ba7d07246ed7f7849f826dd07fc30cbd9e0332a46ff6aacb33da8b3c

See more details on using hashes here.

File details

Details for the file kraken-4.3.12-py3-none-any.whl.

File metadata

  • Download URL: kraken-4.3.12-py3-none-any.whl
  • Upload date:
  • Size: 5.0 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.11.3

File hashes

Hashes for kraken-4.3.12-py3-none-any.whl
Algorithm Hash digest
SHA256 e85e62039d57bc2d0408e333a8df08f666d8000502f9cfd3948f1341f53a07c4
MD5 54dfeb0302ca6758a3925aed64d6f675
BLAKE2b-256 c90fefeba8d380cf5738c7ee5ce124bd4a52815972fd0489a4dae0652f88dc83

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page