手写字符识别的非线性主动判别函数  

Nonlinear Active Discriminant Functions for Handwritten Character Recognition

作　　者：孙广玲[1] 

机构地区：[1]上海大学通信与信息工程学院,上海200072

出　　处：《中国图象图形学报》2008年第10期1853-1856,共4页Journal of Image and Graphics

基　　金：上海市教委项目(A.10-0107-06-018)

摘　　要：提出了用于手写字符识别的非线性主动判别函数,是线性主动判别函数在手写字符非线性变化情况下的推广。该方法利用Kernel PCA分析捕捉和表示这种非线性变化。将输入空间非线性映射为特征空间,在特征空间的主子空间中生成最优主动原型模板,其与字符特征向量在特征空间主子空间的投影之间的距离即为非线性主动判别函数;同时,基于最小分类错误准则对该函数进行了优化。实验结果表明,非线性主动判别函数获得了比线性主动判别函数更高的识别率。A generalization of Linear Active Discriminant Functions named as Nonlinear Active Discriminant Functions （ nonlinear ADF） to deal with nonlinear deformations of handwritten character is proposed. In Nonlinear ADF, Kernel PCA is applied to capture and represent the nonlinear deformations. Input space is mapped to feature space through nonlinear mapping. Then an optimal active prototype model is produced in principal subspaee of the feature space and the distance between it and the projection of character feature vector in the principal subspace is defined as Nonlinear ADF. In addition, the Nonlinear ADF is further optimized using Minimum Classification Error criterion. Experimental results demonstrated that Nonlinear ADF has achieved a higher recognition rate than that of Linear ADF.

关 键 词：字符识别 非线性主动判别函数 KERNEL PCA 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]