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- W320108701 abstract "To improve the reliability of a template-matching classifier for recognizing connected handwritten characters, we present an evolutionary algorithm to optimize handwritten numeral templates represented by rational Bspline surfaces of character pixel-boundary distance maps (PBDMs). Initial templates are extracted by training a feed-forward neural network. In simulation, 1,000 handwritten numeral templates (100 templates for each class) were extracted and optimized using 10,426 training samples (isolated numerals from NIST Special Database 3). A template-matching classifier using these 1,000 optimized templates rejected 90.7% of nonnumeral patterns (not included in the training set) while achieving a correct classification rate of 96.4% on independent isolated numerals." @default.
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- W320108701 date "1999-12-20" @default.
- W320108701 modified "2023-09-23" @default.
- W320108701 title "An Evolutionary Algorithm for Optimizing Handwritten Numeral Templates Represented by Rational B-Spline Surfaces" @default.
- W320108701 doi "https://doi.org/10.20965/jaciii.1999.p0462" @default.
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