摘要
针对传统字符特征提取算法中特征不稳定的缺点,提出一种基于正交盖氏矩的特征提取方法。采用支持向量机解决车牌字符识别问题,自动寻找对分类有较好区分能力的支持向量,由此构成的分类器可以最大化类间间隔,达到正确区分类别的目的。实验结果表明,该方法对于实时视频流中的车牌识别能取得理想效果,在解决有限样本、非线性及高维模式识别问题中表现出优越的性能,且具有适应性强和效率高的特点。
Aiming at 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.
出处
《计算机工程》
CAS
CSCD
2012年第13期192-195,198,共5页
Computer Engineering
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
Gegenbauer moment
feature extraction
character recognition
Support Vector Machine（SVM）
classifier
pattern recognition
