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https://github.com/goncalopp/simple-ocr-opencv

A simple python OCR engine using opencv
https://github.com/goncalopp/simple-ocr-opencv

knn-algorithm machine-learning machine-vision machinelearning machinevision ocr opencv python-ocr supervised-learning

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A simple python OCR engine using opencv

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# Simple Python OCR

[![Build Status](https://travis-ci.org/goncalopp/simple-ocr-opencv.svg?branch=master)](https://travis-ci.org/goncalopp/simple-ocr-opencv)



A simple pythonic OCR engine using opencv and numpy.



Originally inspired by [this stackoverflow question](http://stackoverflow.com/questions/9413216/simple-digit-recognition-ocr-in-opencv-python)



### Essential Concepts



#### Segmentation



In order for OCR to be performed on a image, several steps must be 

performed on the source image. Segmentation is the process of 

identifying the regions of the image that represent characters. 



This project uses rectangles to model segments. 



#### Supervised learning with a classification problem



The [classification problem][] consists in identifying to which class a 

observation belongs to (i.e.: which particular character is contained 

in a segment).



[Supervised learning][] is a way of "teaching" a machine. Basically, an 

algorithm is *trained* through *examples* (i.e.: this particular 

segment contains the character `f`). After training, the machine 

should be able to apply its acquired knowledge to new data.



The [k-NN algorithm], used in this project, is one of the simplest  

classification algorithm.



#### Grounding



Creating a example image with already classified characters, for 

training purposes.

See [ground truth][].



[classification problem]: https://en.wikipedia.org/wiki/Statistical_classification

[Supervised learning]: https://en.wikipedia.org/wiki/Supervised_learning

[k-NN algorithm]: https://en.wikipedia.org/wiki/K-nearest_neighbors_classification

[ground truth]: https://en.wikipedia.org/wiki/Ground_truth



#### How to understand this project



Unfortunately, documentation is a bit sparse at the moment (I 

gladly accept contributions).

The project is well-structured, and most classes and functions have 

docstrings, so that's probably a good way to start.



If you need any help, don't hesitate to contact me. You can find my 

email on my github profile.



#### How to use



Please check `example.py` for basic usage with the existing pre-grounded images.



You can use your own images, by placing them on the `data` directory. 

Grounding images interactively can be accomplished by using `grounding.UserGrounder`.

For more details check `example_grounding.py`



#### Copyright and notices



This project is available under the [GNU AGPLv3 License](https://www.gnu.org/licenses/agpl-3.0.txt), a copy

should be available in LICENSE. If not, check out the link to learn more.

 

    Copyright (C) 2012-2017 by the simple-ocr-opencv authors

    All authors are the copyright owners of their respective additions



    This program is free software: you can redistribute it and/or modify

    it under the terms of the GNU AGPLv3 License, as found in LICENSE.



    This program is distributed in the hope that it will be useful,

    but WITHOUT ANY WARRANTY; without even the implied warranty of

    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the

    GNU General Public License for more details.



    You should have received a copy of the GNU Affero General Public License

    along with this program.  If not, see .