Abstract:

To address the limitation of previous methods in recognizing various types of Chinese character errors and measuring structural similarity, this paper proposes a Chinese character Label Chinese Complete Graph (LCCG) matching method and innovatively introduces an encoding method based on the relative positions and topological structures of strokes. First, the construction process of the Chinese character LCCG is elaborated, and the similarity calculation methods for nodes and edges are defined. The applicable scenarios and specific cost settings for each editing operation are then clearly defined for various error types. Finally, the Max-Min Ant System (MMAS) algorithm is used to determine the optimal matching path, to achieve efficient graph matching. Experimental results show that this method can accurately recognize various types of Chinese character errors and outperforms existing methods in terms of accuracy, matching cost, and running time. Compared to traditional methods, this method not only improves the recognition efficiency significantly, but also enhances the accuracy of stroke matching and calculation of structural similarity through precise editing cost definitions and innovative stroke and structure similarity calculation, demonstrating high practical value.

Key words: Chinese character error types, graph matching, editing operations, Max-Min Ant Colony System (MMAS), stroke structure encoding

摘要：

针对以往方法在汉字多种错误类型识别及结构相似性度量方面的不足, 提出一种汉字标号完全图(LCCG)匹配方法, 并创新性地引入了基于笔画相对位置和拓扑结构的编码方式。首先, 详细阐述汉字LCCG的构建过程, 定义节点与边的相似度计算方式; 然后, 针对多种错误类型, 明确每种编辑操作的适用场景及具体代价设定; 最后, 结合最大最小蚁群系统(MMAS)算法求解最优匹配路径, 实现高效的图匹配。实验结果表明, 该方法能够准确识别多种汉字错误类型, 在准确率、匹配代价和运行时间上均优于现有方法。相比传统方法, 提出的方法不仅显著提升了识别效率, 还通过精确的编辑代价定义和创新的笔画及结构相似性计算方法, 显著增强了汉字笔画匹配的准确性及结构相似性的计算能力, 展现出较高的应用价值。

关键词: 汉字错误类型, 图匹配, 编辑操作, 最大最小蚁群系统, 笔画结构编码