Prototype learning for structured pattern representation applied to on-line recognition of handwritten Japanese characters |
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Authors: | Akihito Kitadai Masaki Nakagawa |
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Affiliation: | (1) Department of Computer, Information and Communication Sciences, Tokyo University of Agriculture and Technology, Naka-cho 2-24-16, Koganei, Tokyo 184-8588, Japan |
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Abstract: | This paper describes prototype learning for structured pattern representation with common subpatterns shared among multiple
character prototypes for on-line recognition of handwritten Japanese characters. Prototype learning algorithms have not yet
been shown to be useful for structured or hierarchical pattern representation. In this paper, we incorporate cost-free parallel
translation to negate the location distributions of subpatterns when they are embedded in character patterns. Moreover, we
introduce normalization into a prototype learning algorithm to extract true feature distributions in raw patterns to aggregate
distributions of feature points to subpattern prototypes. We show that our proposed method significantly improves structured
pattern representation for Japanese on-line character patterns. |
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Keywords: | Online recognition Prototype learning Structured character pattern representation |
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