基于高阶统计的手写字符形变弹性匹配法

Deformable Elastic Matching Based on High Order Statistics for Handwritten Characters Recognition

针对传统弹性匹配法在手写字符识别中存在着由于过匹配而造成误识别的不足,提出一种基于高阶统计的形变弹性匹配法。根据高阶统计量包含字符形状上的细节变化信息,采用独立分量分析抽取出每个字符类的内在变化方向,并将其应用到弹性匹配的形变模型中。字符的任意种形状变化由这组独立分量的线性叠加来表示。通过形变模型,类模板字符发生形变逐次向输入待识别字符趋近,从而在两个字符之间求得一种最佳匹配。在实验结果中,识别率达到92.81%,得到了提高,表明该方法的有效性。

Aiming at the problem of misrecognitions due to overfitting in conventional elastic matching for handwritten character recognitin, a deformable elastic matching approach based on high order statistics is proposed in this paper. According to the handwriting variations in shape details contained in high order statistics, the intrinsic deformations within each character class are extracted from the actual deformations by independent component analysis. Then they are applied to the deformable model. Thus any deformation of a class can be described by the weighted linear combination of the independent components. In this model the prototype character is deformed gradually in an effort to be much closer to the input character. In experimental results, higher recognition rates are obtained with average rate up to 92. 81%, which shows that the proposed approach is very effective for handwritten characters recognition.