blu3s1one / kraken Goto Github PK

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 development version 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 latest stable release 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 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.

Have a look at the docs

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