On-Line Chinese Character Recognition via A Representation of Spatial Relationships between Strokes
On-Line Chinese Character Recognition via A Representation of Spatial Relationships between Strokes
复制标题
通过笔画空间关系表示的在线汉字识别
DOI：
10.1142/s0218001497000159
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发表时间：
1997
影响因子：
1.5
通讯作者：
Suh
中科院分区：
文献类型：
--
作者：
Ju;Suh
汉字是由基本笔画按照结构规则构成的。在手写体字符中，笔画的形状可能会有一定程度的变化，但笔画的空间关系和几何构型通常是保持不变的。因此，这些空间关系和形状可以看作是不变的特征，可以用于手写体汉字的识别。本文对汉字的结构知识进行了研究，提出了汉字的笔画空间关系表示（SSRR）。本文还提出了一种利用SSRR进行联机汉字识别的方法。使用SSRR，每个字符都被处理并由属性图表示。字符识别的过程，从而转化为一个图匹配问题。经过仔细分析，笔画之间的基本空间关系可以分为五类。在数据结构的设计中采用了按位表示法，以减少存储需求和加快字符匹配速度。在预分类中采用分层搜索策略，提高了识别速度。基本上，属性图模型是一个广义的字符表示，提供了一个有用的和方便的表示新添加的字符在OLCCR系统与自动学习能力。分析了利用空间关系进行字符识别的结构化方法的意义，并通过实验加以验证。实际测试结果表明了该方法的有效性。
Chinese characters are constructed by basic strokes based on structural rules. In handwritten characters, the shapes of the strokes may vary to some extent, but the spatial relations and geometric configurations of the strokes are usually maintained. Therefore these spatial relations and configurations could be regarded as invariant features and could be used in the recognition of handwritten Chinese characters. In this paper, we investigate the structural knowledge in Chinese characters and propose the stroke spatial relationship representation (SSRR) to describe Chinese characters. An On-Line Chinese Character Recognition (OLCCR) method using the SSRR is also presented. With SSRR, each character is processed and is represented by an attribute graph. The process of character recognition is thereby transformed into a graph matching problem. After careful analysis, the basic spatial relationship between strokes can be characterized into five classes. A bitwise representation is adopted in the design of the data structure to reduce storage requirements and to speed up character matching. The strategy of hierarchical search in the preclassification improves the recognition speed. Basically, the attribute graph model is a generalized character representation that provides a useful and convenient representation for newly added characters in an OLCCR system with automatic learning capability. The significance of the structural approach of character recognition using spatial relationships is analyzed and is proved by experiments. Realistic testing is provided to show the effectiveness of the proposed method.