OCR of low-resolution text from screenshots

Question

I\'m writing an OCR application to read characters from a screenshot image. Currently, I\'m focusing only on digits. I\'m partially basing my approach on this blog post: http:

Answer 1

OCR on noisy images is not easy - so simple approaches no not work well.

So, I would recommend you to use HOG to extract features and SVM to classify. HOG seems to be one of the most powerful ways to describe shapes.

The whole processing pipeline is implemented in OpenCV, however I do not know the function names in python wrappers. You should be able to train with the latest haartraining.cpp - it actually supports more than haar - HOG and LBP also.

And I think the latest code (from trunk) is much improved over the official release (2.3.1).

HOG usually needs just a fraction of the training data used by other recognition methods, however, if you want to classify shapes that are partially ocludded (or missing), you should make sure you include some such shapes in training.