A noise-adaptive discriminant function and its application to blurred machine-printed kanji recognition

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10 Citations (Scopus)

Abstract

Accurate recognition of blurred images is a practical but previously to mostly overlooked problem. In this paper, we quantify the level of noise in blurred images and propose a new modification of discriminant functions that adapts to the level of noise. Experimental results indicate that the proposed method actually enhances the existing statistical methods and has impressive ability to recognize blurred image patterns.

Original languageEnglish
Pages (from-to)314-319
Number of pages6
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume22
Issue number3
DOIs
Publication statusPublished - 2000

Keywords

  • Bayes classifier
  • Blurred character recognition
  • Discriminant function
  • Distribution of feature vectors
  • Mahalanobis distance
  • Noise

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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