Character Recognition using Hidden Markov Models

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# Character Recognition using Hidden Markov Models - PowerPoint PPT Presentation

Character Recognition using Hidden Markov Models. Anthony DiPirro Ji 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.

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Presentation Transcript

Character Recognition using Hidden Markov Models

Anthony DiPirro

Ji Mei

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

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

S2

Input

S3

S3

S2

S2

Compare

Canonical B

S3

S2

S2

S3

S1

S3

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.