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On-Line Chinese Character Recognition via a Representation of Spatial Relationships between Strokes

机译:通过笔划之间的空间关系表示进行在线汉字识别

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摘要

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.
机译:汉字是根据结构规则通过基本笔划来构造的。在手写字符中,笔画的形状可能会有所变化,但是通常会保留笔画的空间关系和几何形状。因此,这些空间关系和配置可以视为不变特征,可以用于手写汉字的识别。在本文中,我们研究了汉字的结构知识,并提出了用于描述汉字的笔划空间关系表示(SSRR)。还提出了使用SSRR的在线汉字识别(OLCCR)方法。使用SSRR,每个字符都被处理并由属性图表示。字符识别过程由此转化为图形匹配问题。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按位表示,以减少存储需求并加快字符匹配。预分类中的分层搜索策略提高了识别速度。基本上,属性图模型是一种通用字符表示形式,它为具有自动学习功能的OLCCR系统中的新添加字符提供了一种有用且方便的表示形式。分析了利用空间关系进行字符识别的结构方法的重要性,并通过实验证明了这一点。提供了现实的测试来证明所提出方法的有效性。

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