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Online Arabic Handwriting Recognition

KFUPM Information & Computer Science department ICS 482 - Natural Language Processing. Done by. Presented by. Online Arabic Handwriting Recognition. Fadi Biadsy Jihad El-Sana Nizar Habash. Abdul-Rahman Daud. Outline. Introduction 1. Characteristics of the Arabic Script ( problems)

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Online Arabic Handwriting Recognition

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  1. KFUPM Information & Computer Science department ICS 482 - Natural Language Processing Done by Presented by Online Arabic Handwriting Recognition Fadi Biadsy Jihad El-Sana Nizar Habash Abdul-Rahman Daud

  2. Outline • Introduction • 1. Characteristics of the Arabic Script ( problems) • 2. Current Solutions • 3. Better Solution (using HHM) • 4. Hidden Markov Model • Conclusion

  3. Introduction • Best means of human-computer interfacing. • Forms smaller than the traditional • computer use reaches a larger number

  4. Introduction • Speech • More People • Handwriting • Performance • Privacy • Handwriting Categories • Online • offline

  5. Characteristics of the Arabic Script • Cursive: • Arabic is written in a cursive. • style from right to left. • Most letters are written in four

  6. Characteristics of the Arabic Script • Delayed strokes • creating new letters • Dots

  7. Current Solutions • Most of them Offline Handwriting recognition • Strokes are ignored. • Need for effective Online handwriting recognition

  8. Best Solution • Based on HMM • Hidden Markov model • Regular expression (state machines) • Our recognition framework uses discrete HMMs to represent each letter shape.

  9. HMM • (HMM) is a statistical model in which the system being modeled is assumed to be a Markov process with unknown parameters, and the challenge is to determine the hidden parameters from the observable parameters. • The extracted model parameters can then be used to perform further analysis

  10. HMM • Markov process • future states of the process, given the present state and all past states, depends only upon the present state and not on any past states, i.e. it is conditionally independent of the past states • HMM is used for many Patten recognition problems

  11. HMM

  12. HMM • Given the parameters of the model, find the most likely sequence of hidden states that could have generated a given output sequence. This problem is solved by the Viterbi algorithm.

  13. HMM • In this implementation, each observation yi in this observation sequence is an integer value • Letters are joined to from word parts

  14. HMM • To constrain the space of search, we utilize a dictionary of possible valid words. This ensures better recognition rates compared to systems that can recognize any arbitrary permutation of letters.

  15. Conclusion • This solution introduced an HMM based system with to provide solutions for most of the difficulties inherent in recognizing Arabic script: namely delayed strokes.

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