Robust handwritten word recognition with fuzzy sets

Paul Gader, Jung-Hsien Chiang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

A hybrid fuzzy neural system is used to improve a handwritten word recognition algorithm. The word recognition algorithm matches digital images of handwritten words to strings in a lexicon. This algorithm requires a module to assign character class membership values to images of segments of handwritten words. Many of these images are not characters. It is shown that a hybrid neural system consisting of a cascade of a Kohonen Self-Organizing Feature Map (SOFM) followed by Choquet fuzzy integrals can yield improved performance over a multi-layer feedforward network (MLFN). The hybrid method scored a word recognition rate of 85% compared to 77% for the MLFN method.

Original languageEnglish
Title of host publicationProc 3 Int Symp Uncert Model Anal Annu Conf North Amer Fuzzy Inf Process Soc
PublisherIEEE
Pages198-203
Number of pages6
Publication statusPublished - 1995
EventProceedings of the 3rd International Symposium on Uncertainty Modeling and Analysis and Annual Conference of the North American Fuzzy Information Processing Society, (ISUMA - NAFIPS'95) - College Park, MD, USA
Duration: 1995 Sep 171995 Sep 20

Other

OtherProceedings of the 3rd International Symposium on Uncertainty Modeling and Analysis and Annual Conference of the North American Fuzzy Information Processing Society, (ISUMA - NAFIPS'95)
CityCollege Park, MD, USA
Period95-09-1795-09-20

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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  • Cite this

    Gader, P., & Chiang, J-H. (1995). Robust handwritten word recognition with fuzzy sets. In Proc 3 Int Symp Uncert Model Anal Annu Conf North Amer Fuzzy Inf Process Soc (pp. 198-203). IEEE.