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Wavelet-based approach to character skeleton.

Xinge You1, Yuan Yan Tang

  • 1Department of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China. xyou@comp.hkbu.edu.hk

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|May 12, 2007
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Summary
This summary is machine-generated.

This study introduces a novel wavelet transform-based method for character skeletonization, directly extracting central lines and refining singular regions for improved accuracy in character recognition.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Pattern Recognition

Background:

  • Character skeletonization is crucial for character recognition.
  • Traditional methods struggle with direct central line extraction and singular region distortion.
  • Existing algorithms are computationally expensive and lack precision in stroke center identification.

Purpose of the Study:

  • To develop a novel, robust character skeletonization scheme.
  • To overcome limitations of traditional symmetry analysis-based methods.
  • To achieve accurate skeleton extraction for both binary and grayscale images.

Main Methods:

  • A two-step approach utilizing wavelet transform.
  • Direct extraction of primary skeletons in regular regions via wavelet-based symmetry analysis.
  • Amendment processing in singular regions using smooth interpolation and compensation techniques.

Main Results:

  • Successfully extracted character skeletons with precise central line localization.
  • Demonstrated robustness against noise and affine transformations.
  • Achieved positive experimental results on both binary and grayscale images.

Conclusions:

  • The proposed wavelet transform-based skeletonization method offers a direct and efficient solution.
  • The technique effectively handles both regular and singular stroke regions.
  • This method provides a robust and accurate skeleton representation for character recognition.