HMM for perspective estimation in document image, can't understand the algorithm

Posted by maximus on Stack Overflow See other posts from Stack Overflow or by maximus
Published on 2010-04-21T02:33:56Z Indexed on 2010/04/21 2:43 UTC
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image-processing

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hmm

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perspective

Hello! Here is a paper, it is about estimating the perspective of binary image containing text and some noise or non text objects.

The algorithm uses the Hidden Markov Model: actually two conditions T - text B - backgrouond (i.e. noise)

It is hard to understand the algorithm itself. The question is that I've read about Hidden Markov Models and I know that it uses probabilities that must be known. But in this algorithm I can't understand, if they use HMM, how do they get those probabilities (probability of changing the state from S1 to another state for example S2)?

I didn't find anything about training there also in that paper. So, if somebody understands it, please tell me. Also is it possible to use HMM without knowing the state change probabilities?

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