Self-generation Voting Based Method for Handwritten Chinese Character Recognition

Voting strategy is very useful in pattern recognition. Many methods, like Boosting and Bagging, are proposed and are successfully used in some applications using this strategy. However, these methods are infeasible or unsuitable for handwritten Chinese character recognition because of the problem's characteristics. In this paper, a self-generation voting method is proposed for further improving the recognition rate in handwritten Chinese character recognition. This method learns a set of parameters first for generating a set of samples from the test sample, and then classify these generated samples using a base-line classifier. At last, it gives the final recognition result by voting. Experimental results on two databases show that the proposed method is effective and useful in handwritten Chinese character recognition systems.