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Variable duration hidden Markov model and morphological segmentation for handwritten word recognition.

M Y Chen1, A Kundu, S N Srihari

  • 1Dept. Appl. Software, Ind. Technol. Res. Inst., Hsinchu.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1995
PubMed
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This study introduces a novel system for unconstrained handwritten word recognition using a continuous density variable duration hidden Markov model (CD-VDHMM). The system achieves successful recognition results in postal applications.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Pattern Recognition

Background:

  • Handwritten word recognition is challenging due to variations in writing styles.
  • Existing methods often struggle with segmentation ambiguity and variable character durations.

Purpose of the Study:

  • To develop a robust system for unconstrained handwritten word recognition.
  • To address segmentation ambiguity and incorporate linguistic knowledge effectively.

Main Methods:

  • A novel segmentation algorithm based on mathematical morphology is employed.
  • Continuous density variable duration hidden Markov models (CD-VDHMM) are utilized for symbol sequence modeling.
  • A modified Viterbi algorithm and string editing are used for recognition and postprocessing.

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Main Results:

  • The system effectively models character shapes using Gaussian distributions and linguistic constraints via Markov chains.
  • Variable duration states and modified Viterbi algorithm handle segmentation ambiguity.
  • Successful recognition results were achieved in two postal applications.

Conclusions:

  • The proposed CD-VDHMM system offers a comprehensive solution for unconstrained handwritten word recognition.
  • The integration of shape and linguistic information, along with advanced HMM techniques, leads to high accuracy.