Character Recognition using
Hidden Markov Models
Anthony DiPirro
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
– 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
Canonical A
S2
Adjusted
Input
S2
S3
S3
S2
Compare
S3
S2
S3
Canonical B
S2
S2
S1
S3
…
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.