字符识别的置信度分析

Confidence analysis in character recognition

为了能估计字符识别结果的准确性定义了分类器的置信度，提出了广义置信度的概念。证明了基于置信度（或广义置信度）的最优拒识区域选择定理。给出了置信度与识别率的关系。提出了几种常用分类器的广义置信度的估计方法和一种根据广义置信度求出置信度的算法。在理论分析的基础上指出了置信度分析的三个应用：拒识区域的选择、识别率的估计和多分类器的集成。手写数字识别和脱机手写汉字识别的实际应用验证了所提的理论和方法。

In order to estimate the accuracy of character recognition, the defination of classifier's confidence value and the concept of generalized confidence are given. Based on the confidence value the optimal rejection theorem is proven. The relationship between confidence value and recognition rate is disclosed. The formula for evaluating the generalized confidence values for several commonly used pattern classifiers is presented and an algorithm to convert generalized confidence to confidence value is introduced. Three possible applications of confidence analysis: the selection of rejection area, the estimation of recognition rate and the combination of multiple classifier are proposed. Practice in handwritten numeral recognition and off line handwritten Chinese character recognition strongly supports the ideas and the methods.