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Character Recognition using Hidden Markov Models

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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Character Recognition using Hidden Markov Models

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  1. Character Recognition using Hidden Markov Models Anthony DiPirro Ji Mei Sponsor:Prof. William Sverdlik

  2. Our goal • Recognize handwritten Roman and Chinese characters • This is an example of the Noisy Channel Problem Ji

  3. Noisy Channel Problem • Find the intended input, given the noisy input that was received • Examples • iPhone 4S Siri speech recognition • Human handwriting

  4. 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

  5. Hidden Markov Model

  6. Our Project

  7. 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

  8. Pre-Processing • Shrink • Medium filter • Sharpen

  9. Feature Extraction • We count the regions in each area to represent the observation states

  10. Compare Canonical A S2 S2 Adjusted Input S3 S3 S2 S2 Compare Canonical B S3 S2 S2 S3 S1 S3 …

  11. ExperimentingHow to split character

  12. ExperimentingHow to represent states

  13. Result

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

  15. 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.

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