character recognition using hidden markov models

Character Recognition using Hidden Markov Models

Anthony DiPirroJi Mei

Sponsor:Prof. William Sverdlik

Our goal

Recognize handwritten Roman and Chinese characters

This is an example of the Noisy Channel Problem

Ji

Noisy Channel Problem• Find the intended input, given the noisy input

that was received

• Examples

– iPhone 4S 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

Compare

Adjusted Input

Canonical B

Canonical A

…

S2S2

S2 S2

S3

S3 S3

S1

S2S2

S3 S3

ExperimentingHow to split character

ExperimentingHow 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.