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基于Gabor方向特征及神经网络的车牌灰度字符图像识别
Car Plate Gray Character Recognition Using Gabor Orientation Features and Neural Networks
【摘要】 针对低分辨率灰度车牌号码数字识别问题,提出了一种利用网格技术和Gabor变换直接从灰度图像进行特征提取的新方法,并设计了一种集成型神经网络模型来进行识别,对大量的实验数据进行识别实验得到99.26%的识别率,显示该方法是非常有效的。
【Abstract】 A new plate number character recognition method based on Gabor orientation feature and neural networks technologies is presented in this paper. Based on elastic meshing techniques and Gabor filter, a new feature extraction approach for low resolution plate number characters is proposed. An integrated neural networks model is designed as an intelligent classifier. Experiments on large data set produce the recognition rate of 99. 26%, show that the approach is very effective.
【关键词】 Gabor特征提取;
车牌号码字符识别;
集成神经网络;
智能交通系统;
【Key words】 Gabor features extraction; Plate number character recognition; Integrated neural networks; Intelligent transport system(ITS);
【Key words】 Gabor features extraction; Plate number character recognition; Integrated neural networks; Intelligent transport system(ITS);
【基金】 国家自然科学基金资助项目(60275005);广东省自然科学基金资助项目(011611;020828);Motorola国际合作研究基金资助项目(D84110)
- 【文献出处】 计算机工程 ,Computer Engineering , 编辑部邮箱 ,2004年20期
- 【分类号】TP391.41
- 【被引频次】22
- 【下载频次】518