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?
EDIT: May be they are using some estimation, without knowing the HMM parameters (probabilities)