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A genetic algorithm approach to Chinese handwriting normalization.

D S Lin1, J J Leou

  • 1Res. & Dev. Centre, Matsushita Electr. Ind. Co. Ltd., Taipei.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|January 1, 1997
PubMed
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This study introduces a novel genetic algorithm approach for Chinese handwriting normalization. The method optimizes character recognition by intelligently adjusting normalization transforms, improving system accuracy.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Pattern Recognition

Background:

  • Chinese handwriting recognition systems face challenges with variations and distortions.
  • Effective normalization is crucial for simplifying processing and enhancing recognition rates.

Purpose of the Study:

  • To propose a genetic algorithm (GA) based approach for optimizing Chinese handwriting normalization.
  • To improve the accuracy and robustness of Chinese character recognition systems.

Main Methods:

  • A generalized normalization transform was defined as a weighted combination of individual transforms.
  • Genetic algorithms (GAs) were employed to find optimal weighting coefficients for the transforms.
  • The fitness function incorporated three features: stroke density variation (SDV), character area coverage (CAC), and centroid offset (CO).

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Main Results:

  • The proposed GA approach demonstrated feasibility in normalizing Chinese handwriting.
  • Experimental results indicated potential for improved recognition rates compared to traditional methods.
  • The optimized weighting coefficients effectively absorbed writing variations.

Conclusions:

  • The genetic algorithm approach is a viable method for Chinese handwriting normalization.
  • The integration of SDV, CAC, and CO features in the fitness function proved effective.
  • This research contributes to advancing the performance of automated Chinese handwriting recognition.