手写金融汉字识别中的可信度估计

由于实际票据中的手写金融汉字书写得并不规范,而且还会受到票据的背景底纹和印章等因素干扰,所以正确识别率很低.为了达到要求的识别精度,必须拒识大量样本,这样就限制了票据识别系统的自动处理率.本文提出一种基于支持向量机的可信度估计方法,其特点是针对每个文字类别,专门构造用于易混淆字判断与可信度估计的支持向量机.该方法充分利用了支持向量机在少量训练样本情况下对两类判断问题的学习能力,可以准确地估计可信度,从而在拒识较少样本的情况下达到要求的识别精度.

Confidence Evaluation for Handwritten Chinese Financial Character Recognition

Because handwritten characters in practical bank check are always interfered by background image and seals,the accuracy rate of OCR is very low.To obtain required precision,most samples have to be rejected,by which the auto-processing rate is confined.In this paper,a new confidence evaluation method based on support vector machines is proposed.In this method,special SVMs are constructed for every character class to evaluate confidence and recognize similar character.This method makes full use of SVM's studying ability in the condition of few training samples,and can accurately estimate the confidence.By this method we can reject fewer samples to obtain required precision.