基于主动判别函数的手写体识别

提出了一种新的基于主动判别函数的手写体识别方法.首先,提出了主子空间中基于统计特征的主动原型模板并给出了对应于待识模式的最优主动原型模板.然后,提出了主动判别函数,它是两个加权距离之和;其中一个是待识模式与主子空间的距离,另一个是待识模式与次子空间的距离.其次,作为主动判别函数的参数,主动原型模板的约束可应用最小分类错误准则进行优化,这一最优约束有助于提高主动判别函数的识别正确率.最后,在银行票据中使用的手写金融汉字样本库上进行实验.实验结果表明,主动判别函数在手写体识别方面是非常有前景的.

Active Discriminant Function for Handwriting Recognition

A novel recognition method called Active Discriminant Function (ADF) for handwriting recognition is presented. First, statistical feature based Active Prototype Model (APM) in the principal subspace is proposed and an optimal APM corresponding to an unknown pattern is obtained. Second, ADF that is a weighted summation of two distances is proposed. One measures the distance between an unknown pattern and the principal subspace; the other measures the distance between an unknown pattern and the minor subspace. Third, as parameters of ADF, constraints for APM are optimized by applying Minimum Classification Error (MCE) criterion. The optimal constraints help to improve recognition accuracy of ADF. Finally, experiments are conducted on handwritten financial Chinese characters used in bank bill, and empirical results demonstrate that ADF is fairly promising for handwriting recognition.