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Iterative multimodel subimage binarization for handwritten character segmentation.

Amer Dawoud1, Mohamed S Kamel

  • 1Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada. dawoud@watfast.uwaterloo.ca

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|September 29, 2004
PubMed
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This study introduces a novel subimage-based approach for image binarization, enhancing handwritten character recognition. The method iteratively optimizes thresholds for improved binarization quality without prior noise knowledge.

Area of Science:

  • Computer Vision
  • Image Processing
  • Document Analysis

Background:

  • Traditional image binarization methods are limited to global or local thresholding.
  • These methods often struggle with documents exhibiting varying noise levels and character degradation.

Purpose of the Study:

  • To introduce a new category of binarization methods based on subimage analysis.
  • To develop an iterative multimodeling approach for optimizing image binarization.

Main Methods:

  • Image binarization by treating the image as a collection of subimages.
  • Each subimage generates a statistical model for handwritten characters.
  • Iterative optimization of binarization thresholds using gray-level and stroke-run features across subimages.

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Main Results:

  • Significant improvements in binarization quality compared to existing algorithms.
  • Effective application to diverse document types without prior noise information.
  • Demonstrated robustness in handling subimages with unknown noise characteristics.

Conclusions:

  • The proposed subimage-based multimodeling method offers superior binarization performance.
  • This approach provides a flexible and effective solution for document image binarization.
  • It advances the field by introducing a new paradigm beyond global and local methods.