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Nonlinear shape normalization methods for the recognition of large-set handwritten characters

Authors
Journal
Pattern Recognition
0031-3203
Publisher
Elsevier
Publication Date
Volume
27
Issue
7
Identifiers
DOI: 10.1016/0031-3203(94)90155-4
Keywords
  • Handwritten Character Recognition
  • Nonlinear Shape Normalization
  • Feature Projection
  • Feature Density Equalization
  • Performance Evaluation
Disciplines
  • Computer Science
  • Mathematics

Abstract

Abstract Recently, several nonlinear shape normalization methods have been proposed in order to compensate for shape distortions in large-set handwritten characters. In this paper, these methods are reviewed from the two points of view: feature projection and feature density equalization. The former makes feature projection histogram by projecting a certain feature at each point onto horizontal- or vertical-axis and the latter equalizes the feature densities of input image by re-sampling the feature projection histogram. Then, the results of quantitative evaluation for these methods are presented. These methods have been implemented on a PC in C language and tested with a large variety of handwritten Hangul syllables. A systematic comparison of them has been made based on the following criteria: recognition rate, processing speed, computational complexity and degree of variation.

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