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文章基本信息

  • 标题:An Improved Zone Based Hybrid Feature Extraction Model for Handwritten Alphabets Recognition Using Euler Number
  • 本地全文:下载
  • 作者:Om Prakash Sharma ; M. K. Ghose ; Krishna Bikram Shah
  • 期刊名称:International Journal of Soft Computing & Engineering
  • 电子版ISSN:2231-2307
  • 出版年度:2012
  • 卷号:2
  • 期号:2
  • 页码:504-508
  • 出版社:International Journal of Soft Computing & Engineering
  • 摘要:This paper presents an Improved Zone based Hybrid Feature Extraction Model using Euler Number, which not only improves the feature extraction process which was implemented in Diagonal Based Feature Extraction [1] but also helps in efficient classification of the handwritten alphabets. The use of Euler Number in addition to zoning increases the speed and the accuracy of the classifier as we are able to reduce the search space by dividing the character set into three groups.
  • 关键词:Handwritten Character Recognition; Feature;Extraction; Binary Image; Euler Number; Feed Forward Neural;Networks.
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