脱机手写汉字识别中笔段提取算法研究

J4 ›› 2008, Vol. 43 ›› Issue (5): 39-44 .doi:

脱机手写汉字识别中笔段提取算法研究

靳天飞^1,2

1. 山东大学计算机科学与技术学院, 山东济南 250061; 2. 山东建筑大学计算机科学与技术学院, 山东济南 250101

收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2006-10-24 发布日期:2006-10-24
通讯作者: 靳天飞

Sub-stroke extraction research on the off-line hand-written recognition of Chinese characters

JIN Tian-fei^1,2

1. College of Computer Science ＆ Technology, Shandong University, Jinan 250061, Shandong, China; 2. College of Computer Science ＆ Technology, Shandong Jianzhu University, Jinan 250101, Shandong, China

Received:1900-01-01 Revised:1900-01-01 Online:2006-10-24 Published:2006-10-24
Contact: JIN Tian-fei

摘要/Abstract

摘要：

基于目前细化和特征点提取的实现方法,提出了改进的分组细化方法和远端拐点法。改进的分组细化法能够在细化过程中,根据分组数标记字符图像中分叉点的类型,为后续的拐点提取做准备。给出了一种快速提取汉字拐点的方法远端拐点法。实验结果表明,该方法能够较好地提取笔段,特征点提取的正确率达到98.6％。

关键词: 脱机手写体汉字识别;细化;特征点提取;笔段提取

Abstract:

Based on the present realizing method of thinning and feature point extraction, an improved thinning algorithm based on groups and a far inflection-point method was proposed. The method of the improved thinning algorithm can mark the type of branch points in the character images, which is based on group numbers in the thinning process. This method can prepare for sequential sub-stroke extraction. The far inflection-point can provide the method, which could quickly extract a Chinese character inflection-point. Experimental results show that these proposed algorithms can fairly well extract sub-strokes, and the feature point extraction accuracy is 98.6％.

Key words: off-line handwritten recognition of Chinese character; thinning; feature point extraction; sub-stroke extraction

中图分类号:

TP391.43

靳天飞, . 脱机手写汉字识别中笔段提取算法研究[J]. J4, 2008, 43(5): 39-44 .

JIN Tian-fei, . Sub-stroke extraction research on the off-line hand-written recognition of Chinese characters[J]. J4, 2008, 43(5): 39-44 .

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