A simple pythonic OCR engine using opencv and numpy.

Originally inspired by 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 aquired 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.

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 grouding.UserGrounder. For more details see Issue 2