基于多边形逼近的在线汉字识别片段提取算法

Xinqiao Lu, Xiaojuan Liu, Guoqiang Xiao, E. Song, Ping Li, Qiaoling Luo
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引用次数: 2

摘要

提出了一种基于多边形逼近的在线汉字识别片段提取算法。该方法检测内角最小的点,并以此点将整个行程分成两条相邻的曲线,称为截断点或拐点。对两条曲线分别执行相同步骤检测截止点。迭代地执行相同的操作,直到所有曲线中的最小内角大于指定的阈值。所有截断点和起止点组成描边,每一对相邻点构成一个线段。实验证明,该方法具有计算复杂度小、逼近效果好等优点。采用该算法的OLCCR系统实现了20/s的速度和97.2%的识别率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Segment Extraction Algorithm Based on Polygonal Approximation for On-Line Chinese Character Recognition
In this paper, a segment extraction algorithm based on polygonal approximation for on-line Chinese characters recognition (OLCCR) is presented. With this method, the point with the smallest interior angle is detected and the whole stroke is split into two adjacent curves by this point, which is called as a cut-off point or an inflexion. To each of the two curves, the same step is performed to detect the cut-off points respectively. The same operations are performed iteratively until the smallest interior angle in all the curves is larger than an appointed threshold value. All the cut-off points and the start-end points compose the stroke and every pair of adjacent points constructs a segment. Experiments proved that this method has the advantages of less computing complexity and better approximating effect then other methods. An OLCCR system with this segment extraction algorithm has achieved the speed of 20/s and the recognition rate of 97.2%.
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