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)
Perhaps this is a little too academic, more related to applied mathematics than to programming ?
Anyway: HMM are traditionally trained (i.e. learn the value of the parameters of the model, in this case the probabilities) by using some database of already classified data. See the Baum Welch algorithm. This division of two phases: learning (or training) first (with classified-labelled data), classify (or real work) after (with unclassified data) is typical of many algorithms, and it’s called supervised classification.
On the other hand, sometimes we don’t have ‘known’ (classified) data, so we must resort to unsupervised classification, in which we try to learn the model and classify at the same time. This is much more limited, and usually implies making many symplifications and reduction of parameters for the model (so that we don’t have so many things to learn).
At first glance, this seems to be the path taken in that article: they dont consider a fully general HMM, but a very restricted one, and try to find a good fit for the model. But, again, I havent read it seriously.