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Prototype learning for structured pattern representation applied to on-line recognition of handwritten Japanese characters

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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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Authors and Affiliations

  1. Department of Computer, Information and Communication Sciences, Tokyo University of Agriculture and Technology, Naka-cho 2-24-16, Koganei, Tokyo, 184-8588, Japan

    Akihito Kitadai & Masaki Nakagawa

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  1. Akihito Kitadai
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  2. Masaki Nakagawa
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Correspondence to Masaki Nakagawa.

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Kitadai, A., Nakagawa, M. Prototype learning for structured pattern representation applied to on-line recognition of handwritten Japanese characters. IJDAR 10, 101–112 (2007). https://doi.org/10.1007/s10032-006-0036-7

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  • DOI: https://doi.org/10.1007/s10032-006-0036-7

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