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, abbyXML, and hOCR output
- Word bounding boxes and character cuts
- Multi-script recognition support
- Public repository of model files
- Lightweight model files
- Variable recognition network architectures
When using a recent version of pip all dependencies will be installed from binary wheel packages, so installing build-essential or your distributions equivalent is often unnecessary. kraken only runs on Linux or Mac OS X. Windows is not supported.
Install the latest 1.0 release through conda:
$ wget https://raw.githubusercontent.com/mittagessen/kraken/master/environment.yml $ conda env create -f environment.yml
or:
$ wget https://raw.githubusercontent.com/mittagessen/kraken/master/environment_cuda.yml $ conda env create -f environment_cuda.yml
for CUDA acceleration with the appropriate hardware.
It is also possible to install the same version from pypi:
$ pip install kraken
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
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 a binarized image into reading-order sorted lines:
$ kraken -i bw.png lines.json segment
To OCR a binarized image using the default RNN and the previously generated page segmentation:
$ kraken -i bw.png image.txt ocr --lines lines.json
All subcommands and options are documented. Use the help option to get more
information.
Have a look at the docs
kraken is developed at Université PSL.