Handwritten Chinese character identification with Bagged One-Class support vector machines

Today, more and more foreigners go to China and are emerged into studying Chinese. Thereinto, how to write Chinese characters is a very important and difficult task. As computers and internets develop, many teachers for Chinese Education want to use pattern recognition technologies to automatically evaluate and direct the quality of Chinese characters written by foreign students through document scanning. Actually, this is a handwritten character evaluation and identification problem. In this paper, we investigate and compare several character identification methods for Chinese Education within a classification framework. First, some two-class classification techniques with different features and classifiers (BP neural networks and SVMs) are investigated to identify each handwritten Chinese character. Moreover, in character identification, positive examples are always conjunctive, but negative examples are diffused in most cases. Consequently, we use one-class classification technique (one-class SVMs) to perform this handwritten character identification. In order to overcome the sensitivity to the SVM parameters, we propose a variant one-class SVM system - Bagged One-Class SVMs, which integrate many one-class SVMs with sample bagging. Some experiments of evaluating real handwritten Chinese characters by foreigners are performed, which show that general handwritten character identification is a big challenge and one-class classification technique is a potential researching and developing direction.