计算机工程2012，Vol.38Issue(13)：192-195,198,5.DOI:10.3969/j.issn.1000-3428.2012.13.057
基于正交盖氏矩和SVM的车牌字符识别
License Plate Character Recognition Based on Orthogonal Gegenbauer Moment and SVM
王桂文 1孙涵1
作者信息
- 1. 南京航空航天大学计算机科学与技术学院,南京210016
- 折叠
摘要
Abstract
Aiming al the problem that the character features which are got by traditional feature extraction algorithm are not stable, this paper puts forward a feature extraction method based on orthogonal Gegenbauer moment. By using Support Vector Machine(SVM) method to solve the license plate character recognition problem, SVM can automatically search for classification which has good ability to distinguish between the support vector. The classifier can maximize kind of interval, and distinguish the purpose of the category. Experimental results show that this method can make the ideal effect in real-time streaming video of the license plate identification. In solving nonlinear finite sample, and high dimensional pattern recognition problem, it shows many special superior performance, and has strong adaptability and the characteristics of high efficiency.关键词
盖氏矩/特征提取/字符识别/支持向量机/分类器/模式识别Key words
Gegenbauer moment/ feature extraction/ character recognition/ Support Vector Machine(SVM)/ classifier/ pattern recognition分类
信息技术与安全科学引用本文复制引用
王桂文,孙涵..基于正交盖氏矩和SVM的车牌字符识别[J].计算机工程,2012,38(13):192-195,198,5.
