计算机与现代化

• 图像处理 • 上一篇    下一篇

基于改进SURF特征与模糊推理的复杂图片中的文字识别

  

  1. (陕西科技大学电气与信息工程学院,陕西西安710021)
  • 收稿日期:2018-10-30 出版日期:2019-04-26 发布日期:2019-04-30
  • 作者简介:陶筱娇(1989-),女(布依族),贵州都匀人,助理工程师,硕士,研究方向:模式识别,图像处理,E-mail: taoxiaojiao11@163.com; 卢锦(1984-),女,陕西西安人,讲师,博士,研究方向:模式识别。
  • 基金资助:
    陕西省教育厅科研计划项目(17JK0084)

Chinese Character Recognition in Complex Images Based on  #br# Improved SURF Descriptor Features and Fuzzy Reasoning

  1. (College of Electrical & Information Engineering, Shaanxi University of Science & Technology, Xi’an 710021, China)
  • Received:2018-10-30 Online:2019-04-26 Published:2019-04-30

摘要: 鉴于现有文字匹配算法在位置、方向和亮度变化上缺乏足够的鲁棒性,根据汉字结构的特殊性,本文采用改进的SURF算法——SSURF来提取文字特征。首先,计算所有训练样本的SSURF描述符,并将同一类别样本的描述符互相匹配,然后计算匹配次数超过1/2的关键点的匹配率,最后用训练样本SSURF描述符的均值和SSURF描述符与均值的最大欧氏距离来建立类数据库。在识别过程中,计算待识别文字的所有关键点,并将关键点的最大模糊匹配度作为该点的模糊匹配度,最后基于模糊推理实现文字识别。实验结果表明,本文算法识别性能更好。

关键词: 汉字识别, SURF算法, 形状信息, 模糊推理, SSURF算法

Abstract: In considering the robustness of Chinese characters matching to the variations of orientation, position and brightness, this paper describes a new method based on improved Speeded Up Robust Features named SSURF to extract characters features. Firstly, the SSURF descriptors of the same class samples are matched. Then the matching rate of key points whose matching times exceed 1/2 is calculated. Finally, the mean value of SSURF descriptors of training samples and the maximum Euclidean distance between SSURF descriptors and mean values are used to establish class database.Experimental results demonstrate that the proposed method yields a better performance.

Key words: Chinese character recognition, SURF, shape information, fuzzy reasoning, SSURF

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