Hierarchically structured neural networks for printed Hangul character recognition

Sung Bae Cho, Jin H. Kim

Research output: Contribution to conferencePaper

5 Citations (Scopus)

Abstract

A hierarchical neural network which recognizes printed Hangul (Korean) characters is proposed. This system is composed of a type-classification network and six recognition networks. The former classifies input character images into one of the six types by their overall structure, and the latter further classify them into character code. A training scheme including systematic noises is introduced for improving the generalization capabilities of the networks. With the noise-included training, the recognition rate is up to 98.28%, which is superior to the conventional back-propagation network. The neural network approach is very reasonable compared to statistical classifiers and an analysis of generalization capability demonstrates acceptable performance.

Original languageEnglish
Pages265-270
Number of pages6
Publication statusPublished - 1990 Dec 1
Event1990 International Joint Conference on Neural Networks - IJCNN 90 - San Diego, CA, USA
Duration: 1990 Jun 171990 Jun 21

Other

Other1990 International Joint Conference on Neural Networks - IJCNN 90
CitySan Diego, CA, USA
Period90/6/1790/6/21

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All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

Cho, S. B., & Kim, J. H. (1990). Hierarchically structured neural networks for printed Hangul character recognition. 265-270. Paper presented at 1990 International Joint Conference on Neural Networks - IJCNN 90, San Diego, CA, USA, .