基于统计部首模型的联机手写汉字识别方法

利用汉字的部首层次结构有助于减小字符识别器的存储空间和提高泛化性、适应性，但部首分割一直是一个难点.提出一种新的基于部首的联机手写汉字识别方法,该方法把部首形状信息和几何信息集成到识别框架中,在组合搜索过程中利用字符-部首的层次结构字典引导部首的分割与识别,从而提高部首分割的准确率.为克服部首间的连笔,引入角点检测提取子笔划.部首识别采用统计分类器，模型参数通过自学习得到.在字符识别中,采用了2种不同的字典表示以及相应的不同搜索算法.该方法已用于左右与上下结构的字符集，实验结果表明了该方法的有效性.

On-line handwritten Chinese character recognition using statistical radical models

The hierarchical radical structure of Chinese characters can be explored to reduce the number of parameters in character recognition,as well as to improve the generalization ability and adaptability.However,the segmentation of radicals from characters has long been a difficult problem.A new radical-based approach for on-line handwritten Chinese character recognition was proposed.The approach integrated appearance-based radical recognition and geometric context into a principled framework using a hierarchica...