Active Discriminant Function for Handwriting Recognition
基于主动判别函数的手写体识别

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.