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Effects of a Large Amount of Artificial Patterns for On-line Handwritten Japanese Character Recognition

This paper describes effects of a large amount of artificial patterns to train an on-line handwritten Japanese character recognizer. We need a huge amount of pattern samples to train recognizers to achieve high recognition performance for on-line handwritten character recognition. However, the existing pattern samples are not enough. We construct distortion models to generate a large amount of artificial patterns and apply these artificial patterns to train a character recognizer. In experiments using the TUAT Kuchibue database, applying the generated artificial patterns to train the character recognizer has improved the character recognition accuracy remarkably. This method has improved the recognition rate to all the character groups and achieves 95.87% recognition rate for Kanji.