Online Recognition of Chinese Characters: The State-of-the-Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
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This paper describes a prototype learning algorithm for structured character pattern representation with common subpatterns shared among multiple character templates for on-line 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 subpatterns that negates their locationdistributions and normalization that reflects feature distributions in raw patterns to the subpattern prototypes, and then show that a prototype learning algorithm can be applied to the structured character pattern representation with significant effect.