Neural and fuzzy methods in handwriting recognition
Paul Gader, James M. Keller, Raghu J. Krishnapuram, Jung-Hsien Chiang, M.A. Mohamed · Computer · 1997
Handwriting recognition requires tools and techniques that recognize complex character patterns and represent imprecise, common-sense knowledge about the general appearance of characters, words and phrases. Neural networks and fuzzy logic are complementary tools for solving such problems. Neural networks, which are highly nonlinear and highly interconnected for processing imprecise information, can finely approximate complicated decision boundaries. Fuzzy set methods can represent degrees of truth or belonging. Fuzzy logic encodes imprecise knowledge and naturally maintains multiple hypotheses that result from the uncertainty and vagueness inherent in real problems. By combining the complementary strengths of neural and fuzzy approaches into a hybrid system, we can attain an increased recognition capability for solving handwriting recognition problems. This article describes the application of neural and fuzzy methods to three problems: recognition of handwritten words; recognition of numeric fields; and location of handwritten street numbers in address images.