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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
slide1

Character Recognition using Hidden Markov Models

Anthony DiPirro

Ji Mei

Sponsor:Prof. William Sverdlik

slide2

Our goal

  • Recognize handwritten Roman and Chinese characters
  • This is an example of the Noisy Channel Problem

Ji

slide3

Noisy Channel Problem

  • Find the intended input, given the noisy input that was received
  • Examples
      • iPhone 4S Siri speech recognition
      • Human handwriting
slide4

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
slide8

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
slide9

Pre-Processing

  • Shrink
  • Medium filter
  • Sharpen
slide10

Feature Extraction

  • We count the regions in each area to represent the observation states
slide11

Compare

Canonical A

S2

S2

Adjusted

Input

S3

S3

S2

S2

Compare

Canonical B

S3

S2

S2

S3

S1

S3

slide15

Conclusions

  • Factors that will affect accuracy
    • Pre-processing
    • How to split word
    • Number of states
slide16

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.