摘要
针对智能交通系统中中文车牌图像中字符识别准确率不高速率低的问题，根据中文车牌字符图像纹理特点改进经典的局部二值模式（ LBP），并在此基础上提出一种中文车牌字符识别高效算法。该方法采用改进的局部纹理算子LBP描述车牌字符，对于中文、字母、数字这三种类型字符分别使用不同维数扩展的LBP特征描述，并通过多层感知器（ MLP）分类算法识别字符，因此同时结合了LBP和MLP算法的优势。实验结果表明，与工业上常用车牌字符识别算法相比，所提方法字符识别更准确，准确率约96.5%，同时识别时间比其他常用算法缩短了24%~62%，可满足智能交通系统实时性与准确性的求。
Since Chinese license plate is not easy to recognize fast and accurately in Intelligent Transportation System （ ITS） , the typical Local Binary Pattern （ LBP） was improved based on the image characteristics of Chinese license plate character, and then an effective Chinese license plate recognitiosn approach was proposed. The improved LBP was applied to describe the characters in the license plate, in which different dimension of LBP features were extended for Chinese characters, English letters and digits, the three different types of characters in Chinese license plate. And the Multi-Layer Perceptron （ MLP） classification method was applied to recognize the characters. So it combined the advantages of both LBP and MLP. Experimental Results show that compared with other common state-of-the-art algorithms, the proposed algorithm is more accurate, with the recognition accuracy of about 96. 5%, and the recognition time reduces by 24% -62%, thus the approach could satisfy the real-time and accurate demands of the ITS.
出处
《计算机应用》
CSCD
北大核心
2015年第A01期283-285,304,共4页
journal of Computer Applications
基金
国家发改委项目(发改办高技[2012]1424号)
关键词
智能交通系统
局部二值模式
多层感知器
车牌
字符识别
训练
分类
Intelligent Transportation System （ITS）
Local Binary Pattern （LBP）
Multi-Layer Perceptron （MLP）
license plate
character recognition
training
classification
