摘要
车牌识别系统的关键在于字符的识别,字符识别的核心是提取字符特征。小波变换可以获取字符的细节结构特征,不变矩能很好地对其进行描述,将两者结合起来提取字符的特征。利用张量积小波分解高频子图具有方向性的特点,提取字符的笔画特征,得到反映字符结构和统计特征的联和特征向量.字符图像的分解采用第二代提升小波算法,进一步降低了计算复杂性。实验结果表明,此方法提取得到的联合特征向量能达到98%的字符识别率,可以满足实际应用的要求。
The key of license plate recognition system is character recognition, and the core of character recognition is to extract character features. Wavelet transform can obtain the details and structure features for characters, and the invariant moment can describe features. The character strokes feature is extracted it well, which are combined to extract the character by using the directional feature of high frequency sub-images which are decomposed by tensor product wavelet, and the alliance feature vector reflecting the structural and statistical features of characters is obtained. The decomposition of character images adopts the second generation lifting wavelet algorithm, which further reduces the computational complexity. Experiment results show that the alliance feature vector extracted by the proposed method can achieve 98% character recognition rate, which can meet the requirements of practical application.
出处
《量子电子学报》
CAS
CSCD
北大核心
2016年第6期662-670,共9页
Chinese Journal of Quantum Electronics
基金
国家自然科学基金面上项目(61471160)
湖北省自然科学基金重点项目(2012FFA053)~~
关键词
信息处理
模式识别
提升小波变换
不变矩
统计特征
结构特征
笔画特征
information processing
pattern recognition
lifting wavelet transform
invariant moment
statistical features
structural features
stroke features
