为了提高手写汉字的识别率和降低训练时间，提出了一种基于多通道PCA(Principal component analysis)模型的手写汉字识别方法．该方法首先根据汉字的结构特点，将手写汉字分解为“一”、“I”、“J”、“＼”4种方向子模式，然后分别对每个子模式进行主分量分析，最后通过建立起每类汉字的多通道PCA模型来进行手写汉字的识别．该方法既兼顾了主分量对手写汉字的描述能力，又有效地降低了建立模型的训练时间．针对1034类别的手写汉字样本的实验结果表明，该汉字识别方法的识别率较欧氏距离分类器提高了4．4个百分点，而其训练时间则明显低于直接进行PCA重建的识别方法，由此可见，该方法是有效的。

In this paper, a new approach for handwritten Chinese character recognition based on multi-channel PCA (principal component analysis)model is proposed. In terms of the stroke directional characteristics of the handwritten characters, a handwritten Chinese character is decomposed into the four directional sub-patterns at first, namely, horizontal (一), vertical(丨), left up diagonal (丿) and right up diagonal( )sub-pattern, each of which could be modeled by its principal components. Then, based on their four sub-pattern PCA models, a multi-channel PCA model for each category of the handwritten Chinese character is constructed respectively, and the model's reconstruction error is used as a matching measure for the handwritten Chinese character recognition. The method can not only exploit principal components' ability for representing the handwritten Chinese character sample set, but also effectively reduce the training time for modeling. Experimental results on 1034 categories of handwritten Chinese characters indicate that, the proposed method can improve recognition rate by 4.4% comparing to the Euclidean distance classifier, while its training time is much lower than that for modeling handwritten Chinese character directly by its PCA model, showing the effectiveness of the proposed approach.