OCR/HTR engine for all the languages
Project description
Description
kraken is a fork of ocropus intended to rectify a number of issues while preserving (mostly) functional equivalence. Its main features are:
Script detection and multiscript recognition support
Right-to-Left, BiDi, and Top-to-Bottom script support
ALTO, abbyXML, and hOCR output
Word bounding boxes and character cuts
Public repository of model files
Dynamic recognition model architectures and GPU acceleration
Clean public API
Installation
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.
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 default
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 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.
Documentation
Have a look at the docs
Funding
kraken is developed at Université PSL.
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