Comparison of Crisp and Fuzzy Character Neural Networks in Handwritten Word Recognition

Paul Gader, Magdi Mohamed, Jung Hsien Chiang

Research output: Contribution to journalArticlepeer-review

41 Citations (Scopus)


Experiments comparing neural networks trained with crisp and fuzzy desired outputs are described. A handwritten word recognition algorithm using the neural networks for character level confidence assignment was tested on images of words taken from the United States Postal Service mailstream. The fuzzy outputs were defined using a fuzzy k-nearest neighbor algorithm. The crisp networks slightly outperformed the fuzzy networks at the character level but the fuzzy networks outperformed the crisp networks at the word level. This empirical result is interpreted as an example of the principle of least commitment.

Original languageEnglish
Pages (from-to)357-363
Number of pages7
JournalIEEE Transactions on Fuzzy Systems
Issue number3
Publication statusPublished - 1995 Aug

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


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