基于层次分类的脱机手写字符识别

人类在进行字符识别活动时,会根据对象复杂度的不同,采用不同的识别方法.对于结构简单的字符,利用宏观整体信息识别;对于易混淆的形近字,利用微观具体信息区分.为了模拟人类智能进行字符识别活动的过程,设计了一种基于层次分类的脱机手写字符识别算法.该算法将分类器划分为宏观层和微观层,宏观层模拟简单字符识别过程,利用基于梯度的统计特征描述整体信息,完成识别;微观层模拟形近字识别过程,利用基于主曲线的结构特征描述具体信息,完成区分.算法还引入了可信度概念,用以量度推理过程及识别结果的不确定性程度.给出了形近字的定义及区分规则.实验表明,提出的算法有效地提高了脱机手写字符的识别率,对形近字的区分效果尤佳.

Off-line Handwritten Character Recognition Based on Hierarchical Classification

The paper proposed a method of off-line handwritten character recognition based on hierarchical classification. The method simulates the produce of character recognition of human. When a man wants to recognize a character,he uses different strategy in different situation. If the character has a simple structure, he uses global features; if it looks similar with other character,he uses local features. We divided the classifier into macro layer and micro layer. The macro layer uses gradient feature to represent global feature, it simulates the simple target recognition produce; the micro layer uses principal curve feature to represent local feature, it simulates the similar form character recognition produce. We used confidence value to measure indeterminacy of the produce and result. We gave the definition of similar form character,and rules to telling them. The experimental results indicate that the method can effectively improve the recognition rate of off-line handwritten character, especially well in telling similar form character.