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Network-based approach to online cursive script recognition.

B K Sin1, J Y Ha, S C Oh

  • 1Multimedia Res. Labs., Korea Telecom, Seoul.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|February 7, 2008
PubMed
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This study combines Hidden Markov Models (HMMs) with dynamic programming for efficient online handwriting recognition. This approach simultaneously recognizes characters and segments letters without extra computational cost.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Pattern Recognition

Background:

  • Online handwriting recognition systems often require separate steps for character recognition and letter segmentation.
  • Hidden Markov Models (HMMs) are effective for sequence modeling but integrating them into complex word networks can be challenging.

Purpose of the Study:

  • To develop an integrated approach for online handwriting recognition using a network of HMMs and dynamic programming.
  • To achieve simultaneous character recognition and letter segmentation without additional computational overhead.

Main Methods:

  • Constructing a word model by systematically concatenating letter and ligature HMMs, allowing for shared components.
  • Defining character recognition as finding the best alignment path within the HMM network using dynamic programming.

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Main Results:

  • The proposed method successfully integrates HMM networks with dynamic programming for online handwriting recognition.
  • Letter segmentation is achieved concurrently with character recognition, demonstrating computational efficiency.

Conclusions:

  • Combining HMM networks and dynamic programming offers a powerful and efficient solution for online handwriting recognition.
  • The simultaneous segmentation and recognition capability presents a significant advantage over traditional methods.