A simple pythonic OCR engine using opencv and numpy.

Originally inspired by this stackoverflow question

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.

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.

Creating a example image with already classified characters, for training purposes. See ground truth.

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.

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

This project is available under the GNU AGPLv3 License, 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 <http://www.gnu.org/licenses/>.