An emission probability in my case is P(image|letter) ... the probability of the image given that you know it is of a particular character. In other words, the probability of a certain letter looking like this. The way I've been calculating this probability is P(letter|image), since I've been doing my own version of the nearest neighbor density. P(letter|image ) != P(letter|image).. this was wrong! I can use Bayes' rule to turn the probability around though:
P(image|letter) = P(letter|image)P(image) / P(letter)
I don't know what to plug in for P(image) so for now I've just been using 1 - each image has equal probability. For P(letter), this is easy to find - just look at how many times each letter appears in the roster and divide by the total number of characters. Since P(image) = 1, my new equation is:
P(image|letter) = P(letter|image) / P(letter)
I noticed that if a letter has high probability, P(image|letter) will go down (compared to a less likely character. The probability will go up because the denominator will always be <= 1) and if P(letter) is very low then P(image|letter) will be very high.
My results became worse once I took this new probability into account.
Subscribe to:
Post Comments (Atom)
1 comment:
Hi there, I am doing my project of character recognition using HMM.I am using Matlab for the same, but don't have any idea that what will be the input & how the character will get recognised.Since very little study material is available on HMM.
Kindly Suggest any books on HMM for character recognition.If possible pls pload your code.
Thanks.
Prasenjeet
Post a Comment