Structural learning of character patterns for on-line recognition of handwritten Japanese characters
This paper describes the structural learning of Kanji patterns in on-line handwritten character recognition. Upon the request to learn an input pattern, the system investigates which subpattern or the pattern as a whole is non-standard, registers the (sub)pattern and extends the effect of the registration to all the character categories whose shapes include it. A character pattern representation dictionary stores character patterns as combinations of subpatterns so that a common subpattern is shared by all the character categories which include it in their shapes. The recognizer constructs all template patterns from its constituent subpatterns for matching them with an input pattern. Registration of a character pattern invokes identification and registration of a non-standard subpattern in it so that the effect extends to all the characters whose shapes include it. A preliminary evaluation shows it is highly effective without any bad side effect.
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