A learning algorithm for structured character pattern representation used in online recognition of handwritten Japanese characters

Abstract

This paper describes a prototype learning algorithm for structured character pattern representation with common sub-patterns shared among multiple character templates for online recognition of handwritten Japanese characters. Although prototype learning algorithms have been proved useful for an unstructured set of features, they have not been presented for structured or hierarchical pattern representation. In this paper, we present cost-free parallel translation without rotation of sub-patterns that negates their location distributions and normalization that reflects feature distributions in raw patterns to the sub-pattern prototypes, and then show that a prototype learning algorithm can be applied to the structured character pattern representation with significant effect.