华南理工大学学报(自然科学版) ›› 2007, Vol. 35 ›› Issue (1): 70-73,79.

• 电子、通信与自动控制 • 上一篇    下一篇

基于动态规划的联机手写汉字分割方法

高学   

  1. 华南理工大学 电子与信息学院,广东 广州 510640
  • 收稿日期:2005-12-26 出版日期:2007-01-25 发布日期:2007-01-25
  • 通信作者: 高学(1967-) ,男,博士,讲师,主要从事图像处理与模式识别方面的研究。 E-mail:xuegao@scut.edu.cn
  • 作者简介:高学(1967-) ,男,博士,讲师,主要从事图像处理与模式识别方面的研究。
  • 基金资助:

    广东省自然科学基金资助项目(04300098)

Dynamic Programming-Based Online Handwritten Chinese Character Segmentation Approach

Gao Xue   

  1. School of Electronic and Information Engineering , South China Univ. of Tech. , Guangzhou 510640 , Guangdong , China
  • Received:2005-12-26 Online:2007-01-25 Published:2007-01-25
  • Contact: 高学(1967-) ,男,博士,讲师,主要从事图像处理与模式识别方面的研究。 E-mail:xuegao@scut.edu.cn
  • About author:高学(1967-) ,男,博士,讲师,主要从事图像处理与模式识别方面的研究。
  • Supported by:

    广东省自然科学基金资助项目(04300098)

摘要: 为解决手写汉字文本的自动切分问题,提出了一种基于动态规划的联机手写汉字分割方法.该方法根据手写笔画的结构特征、笔顺信息以及神经网络分类器给出的类概率构造代价函数,并将其分别应用于手写句子的预分割和基于识别的分割过程,然后利用动态规划算法寻找最佳分割路径.预分割在保持较低误分割率的前提下,可以有效地降低候选分割块的数量,以加速分割过程.实验结果表明,预分割的误分割率为0.57% ,过分割率仅为1 1. 1 % ;在未应用语言模型的情况下,最终的正确分割率为88.2%。

关键词: 字符识别, 动态规划, 联机手写汉字分割, 结构特征

Abstract:

In order to solve the problem of automatic segmentation of handwritten Chinese text , a dynamic program-ming-based online handwritten Chinese character segmentation method is proposed. In this method , the geometrical features of handwritten strokes , the stroke sequence information and the class probability given by the neural net-work classifier are used to construct a segmentation cost function , and are then applied to the pre-segmentation and recognition-based segmentation stages of handwritten sentences , respectively. After that , the dynamic programming algorithm is adopted to find the optimal segmentation path. The pre-segmentation can effectively reduce the amount of segmentation hypothesis with a reasonable incorrect segmentation rate , thus speeding up the segmentation. Ex-perimental results show that the pre-segmentation stage achieves an incorrect segmentation rate of O. 579% and an over-segmentation rate of 11. 1 % , and that the final correct segmentation rate is 88. 2 % without using any language model.

Key words: character recognition, dynarnic programming, online handwritten Chinese character segmentation, geo-metrical feature