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simple-ocr-opencv (0, 2019-03-09)
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# Simple Python OCR [](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 .

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