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Character Recognition using Hidden Markov Models
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Transcript of 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.