用矩特征进行光字符识别的一种新方法

MOMENT - BASED OPTICAL CHARACTER RECOGNITION

模式识别是信息科学及其应用中的一个非常活跃的领域,各种特征提取及识别方法层出不穷。本文在分析已有特征提取技术的弊端的基础上,提出用规格化后的中心矩作为特征向量的新技术,并将其应用于光字符识别中。规格化后的中心矩具有平移不变性和伸缩不变性,因而提高了识别率。在本光字符识别系统中,给出了矩特征向量的选取方法并确定了分类策略。实验表明,当使用8或10行字符的特征向量进行判别时,可得到超过95％的识别率。

Pattern recognition is an active field in information science with various methods for feature extraction and object recognition. In this paper, the author uses normalized central moments as feature vector after examining the shortcomings of existing feature extraction techniques. Application of the proposed technique to optical character recognition is given. The normalized central moments possess the properties of translation - invari-ance and scale- invariance, hence improve recognition accuracy.The author not only selects moment feature vector that can provide good discrimination between characters,but also determines classification strategy.Experiments show that we can obtain bigger than 95% increase in recognition rates when we use eight or ten library sets.