Effects of Generating a Large Amount of Artificial Patterns for On-line Handwritten Japanese Character Recognition
Bin Chen, Bilan Zhu, Masaki Nakagawa · 2011
This paper describes effects of a large amount of artificial patterns to train an on-line handwritten Japanese character recognizer. In general, as more learning patterns employed for training pattern recognition systems, as higher recognition rate is obtained. In reality, however, the existing pattern samples are not enough, especially for languages of a large character set. Therefore, for on-line handwritten Japanese character recognition, we construct six linear distortion models and combine them with a nonlinear distortion model to generate a large amount of artificial patterns. We apply the method for the TUAT Nakayosi database and train a recognizer while evaluate the effects for the TUAT Kuchibue database with the remarkable effects of improving recognition accuracy.