Character Recognition using Hidden Markov Models Anthony Di

Character Recognition using Hidden Markov Models Anthony Di. Pirro Ji Mei Sponsor: Prof. William Sverdlik

Our goal Recognize handwritten Roman and Chinese characters Ji This is an example of the Noisy Channel Problem

Noisy Channel Problem • Find the intended input, given the noisy input that was received • Examples – i. Phone 4 S Siri speech recognition – Human handwriting

Markov Chain We use a Hidden Markov Model to solve the Noisy Channel Problem A HMM is a Markov chain for which the state is only partially observable. Markov Chain Definition Illustration

Hidden Markov Model

Our Project

How to solve our problem? • Using a HMM, we can calculate the hidden states chain, based on the observation chain • We used our collected samples to calculate transition probability table and emission probability table • Use Viterbi algorithm to find the most likely result

Pre-Processing • Shrink • Medium filter • Sharpen

Feature Extraction • We count the regions in each area to represent the observation states

Compare Canonical A S 2 Adjusted Input S 2 S 3 S 2 Compare S 3 S 2 S 3 Canonical B S 2 S 1 S 3 …

Experimenting How to split character

Experimenting How to represent states

Result

Conclusions • Factors that will affect accuracy – Pre-processing – How to split word – Number of states

In the future • Spend more time on different features Pixel Density Counting lines • Use other algorithms such as a neural network to implement character recognition.