Fuzzy and crisp handwritten character recognition using neural networks

Paul Gader, Magdi Mohamed, Jung Hsien Chiang

研究成果: Paper同行評審

5 引文 斯高帕斯(Scopus)

摘要

In this paper, we describe a neural-network-based handwritten alphabetic character recognition system. Feature vectors are generated using a two-pass algorithm to generate direction magnitude images followed by summing over overlapping zones. Crisp neural networks and fuzzy neural networks are trained using back-propagation. In the crisp case, the desired output is high for the correct class and low for all others. In the fuzzy case, the desired outputs are set using a fuzzy k-nearest neighbor algorithm.

原文English
頁面421-426
頁數6
出版狀態Published - 1992 十二月 1
事件Proceedings of the 1992 Artificial Neural Networks in Engineering, ANNIE'92 - St.Louis, MO, USA
持續時間: 1992 十一月 151992 十一月 18

Other

OtherProceedings of the 1992 Artificial Neural Networks in Engineering, ANNIE'92
城市St.Louis, MO, USA
期間92-11-1592-11-18

All Science Journal Classification (ASJC) codes

  • 軟體

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