Understanding Freeman chain codes for OCR

Question

Note that I\'m really looking for an answer to my question. I am not looking for a link to some source code or to some academic paper: I\'ve already used the s

Answer 1

You could convert the chain code into an even simpler model that conveys the topology and then run machine learning code (which one would probably write in Prolog).

But I wouldn't endorse it. People have done/tried this for years and we still have no good results.

Instead of wasting your time with this non-linear/threshold based approach, why don't you just use a robust technique based on correlation? The easiest thing would be to convolve with templates.

But I would develop Gabor wavelets on the letters and sort the coefficients into a vector space. Train a support vector machine with some examples and then use it as a classifier.

This is pretty much how our brain does it and I'm sure its possible in the computer.

Some random chit chat (ignore):

I wouldn't use neuronal networks because I don't understand them and therefore don't like them. However, I'm always impressed by work of Geoff Hintons group http://www.youtube.com/watch?v=VdIURAu1-aU.

Somehow he works on networks that can propagate information backward (deep learning). There is a talk of him where he lets a trained digit recognition network dream. That means he sets one of the output neurons to "2" and the network will generate pictures of things that it thinks are two on the input neurons.

I found this very cool.