Computer Engineering and Applications ›› 2008, Vol. 44 ›› Issue (26): 196-199.DOI: 10.3778/j.issn.1002-8331.2008.26.060

• 图形、图像、模式识别 • Previous Articles     Next Articles

Edge color-clustering and neural network-based license plate style identification

WANG Hai-jiao1,LI Wen-ju1,WANG Xin-nian2,JIA Xiao-dan1   

  1. 1.Department of Computer and Information Technology,Liaoning Normal University,Dalian,Liaoning 116029,China
    2.Department of Information Sciences and Technology,Dalian Maritime University,Dalian,Liaoning 116026,China
  • Received:2007-11-06 Revised:2008-01-22 Online:2008-09-11 Published:2008-09-11
  • Contact: WANG Hai-jiao

基于边缘颜色聚类和神经网络的车牌类型识别

王海姣1,李文举1,王新年2,贾晓丹1   

  1. 1.辽宁师范大学 计算机与信息技术学院,辽宁 大连 116029
    2.大连海事大学 信息科学技术学院,辽宁 大连 116026
  • 通讯作者: 王海姣

Abstract: License plate recognition is the key of the intelligent transport monitoring system,and the rate of license plate style identification is one of the important technical specifications in the license plate recognition system.This paper proposes a new method based on clustering and neural network to identify the license plate style.Firstly,the plate image is incline corrected,and then the effective region is extracted,finally the plate style is identified by the K-means and BP neural network.The experimental results of more than 500 images acquired under different conditions show that the method is more accurate and robust to noise and illumination variations,and the rate of identification is above 99%.

摘要: 汽车牌照自动识别系统是实现智能化道路车辆监控的基础,而车牌类型的识别率是车牌识别系统中重要的技术指标之一。提出了一种基于聚类和神经网络车牌类型识别算法。首先进行车牌的倾斜校正,其次提取车牌的有效区域,最后应用K-means边缘颜色聚类和两级BP神经网络进行车牌类型的识别。对各种条件下采集的500幅车牌图像进行实验,识别率在99%以上。实验结果表明,该算法对光照变化和噪声具有很好的鲁棒性。