A fuzzy model for unsupervised character classification

Shy Shyan Chen, Frank Y. Shih, Peter A. Ng

Research output: Contribution to conferencePaperpeer-review

1 Scopus citations

Abstract

This paper presents a fuzzy-logic approach to the efficient unsupervised character classification in order to increase the robustness, correctness, and speed of a follow-up optical character recognition system. The classification procedures are split into two stages. The first stage separates the characters into seven categories based on the word structure of a text line. The second stage, referring to pattern matching, is to classify all the characters in each category of stage one into a different set of prototypes. The existing methods of similarity measures and their problems are investigated, and a nonlinear weighted similarity function is proposed. A fuzzy model of unsupervised classification, which is more natural to represent the library of prototypes, is defined and the weighted fuzzy similarity measure is extended. Several propositions of the features of the fuzzy model are discussed. Finally, a preclassifier to speed up the classification is presented. The small set of prototypes can be recognized and postprocessed much easier and more efficient.

Original languageEnglish (US)
Pages70-72
Number of pages3
StatePublished - 1993
EventSecond International Conference on Fuzzy Theory and Technology Proceedings, Abstracts and Summaries - Durham, NC, United States
Duration: Oct 13 1993Oct 16 1993

Other

OtherSecond International Conference on Fuzzy Theory and Technology Proceedings, Abstracts and Summaries
CountryUnited States
CityDurham, NC
Period10/13/9310/16/93

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Software
  • Computational Theory and Mathematics
  • Artificial Intelligence

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