基于小波分析的车牌识别关键方法研究
|
摘要
作为现代智能交通系统中的一项非常重要的技术,汽车牌照自动识别技术是近年来的研究热点。智能交通系统在车辆跟踪与提取、高速公路自动收费、停车场自动计费以及交通流量统计等方面能发挥重要的作用,而牌照是作为交通系统个体的车辆的唯一标志,因此车牌自动识别技术在整个系统中处于核心的关键地位,具有实际而重要的研究意义。国内外的研究人员已经和正在就车牌自动识别技术展开广泛而深入地研究,提出了许多算法和方案,并有一些产品投入使用,其效果都未能达到人们所期望的水平,离真正实用的要求还有一定的差距。因此在提高车牌提取及识别算法的准确率,健壮性和实时性方面,还存在较大的研究空间。
本文在充分研究前人的研究成果基础上,结合新兴的小波分析技术完成了车牌识别系统的方法研究和设计工作,主要工作有以下几个部分:(1)车牌提取(2)字符分割(3)字符识别。
在车牌提取方面,本文针对各种复杂背景的车牌图像,对车牌候选区域图像进行小波分析从而准确地提取出车牌位置。经过实验证明,该算法达到了预期的效果,从原始图像中提取出车牌的准确度超过98%。
在字符分割方面,本文先采用区域生长算法得到个别字符的位置,然后再利用车牌图像的竖直投影和先验知识来修正字符的位置,最后用得到字符的位置进行字符分割。该算法的准确率和抗干扰性要比传统的投影算法要好得多。在字符识别方面,本文采用发展非常成熟的BP神经网络来完成字符分类工作,但在某些细节上对BP神经网络进行优化,使本系统的的BP神经网络收敛性好,训练速度快。
本文提到的所有算法皆已正确实现并仿真成功,使用从各种不同环境中采集了多幅含有车牌的图像作为数据源,数据源具有相当的代表性。经过测试,系统的整体识别率达到了92.7%。所有这些都表明本文的算法识别率较高,速度较快,并且具有相当的鲁棒性。
As one of the very important techniques in modern intelligent transportation system, the automatic vehicle license plate recognizing technique is the hot spot of recent years' research. Intelligent transportation system plays an important role in vehicle tracking and locating、high way toll collecting、automatic parking fee charging and transportation statistic. Because license plate is the only identity of each vehicle, the technique of automatic license plate recognizing stands at a key position in the modern transportation system. Researchers at home and overseas have done extensive works on license plate recognizing, and proposed many algorithms and proposals. Many products were put into practical use, but they never achieved the performance as people had expected. There are many works need to be done to improve the accuracy of plate locating and recognizing, robustness and real-time ability.
On the basis of other people’s research, this paper accomplished research and design of vehicle LPR system. This paper’s theory foundation is the new theory——wavelet analysis. The system consists of three main modules: (1) Vehicle license plate extraction; (2) Character segmentation; (3) Character recognition.
In plate extraction module, this paper employs wavelet analysis to extract the number plate from the complicated background image. The algorithm locates the plate correctly based on wavelet analysis. According to the experiment, the algorithm achieves the goal and the rate of plate location of this system is over 98%.
In the character segmentation module, area growth algorithm is adopted, and plate image vertical projection is combined to segment the character. The algorithm is much better than the traditional methods based on projection. In the character recognition module, the traditional character recognition method based on neural network is adopted in this paper. But the BP neural network has been modified a little and the performance of the BP neural network is improved.
本文在充分研究前人的研究成果基础上,结合新兴的小波分析技术完成了车牌识别系统的方法研究和设计工作,主要工作有以下几个部分:(1)车牌提取(2)字符分割(3)字符识别。
在车牌提取方面,本文针对各种复杂背景的车牌图像,对车牌候选区域图像进行小波分析从而准确地提取出车牌位置。经过实验证明,该算法达到了预期的效果,从原始图像中提取出车牌的准确度超过98%。
在字符分割方面,本文先采用区域生长算法得到个别字符的位置,然后再利用车牌图像的竖直投影和先验知识来修正字符的位置,最后用得到字符的位置进行字符分割。该算法的准确率和抗干扰性要比传统的投影算法要好得多。在字符识别方面,本文采用发展非常成熟的BP神经网络来完成字符分类工作,但在某些细节上对BP神经网络进行优化,使本系统的的BP神经网络收敛性好,训练速度快。
本文提到的所有算法皆已正确实现并仿真成功,使用从各种不同环境中采集了多幅含有车牌的图像作为数据源,数据源具有相当的代表性。经过测试,系统的整体识别率达到了92.7%。所有这些都表明本文的算法识别率较高,速度较快,并且具有相当的鲁棒性。
As one of the very important techniques in modern intelligent transportation system, the automatic vehicle license plate recognizing technique is the hot spot of recent years' research. Intelligent transportation system plays an important role in vehicle tracking and locating、high way toll collecting、automatic parking fee charging and transportation statistic. Because license plate is the only identity of each vehicle, the technique of automatic license plate recognizing stands at a key position in the modern transportation system. Researchers at home and overseas have done extensive works on license plate recognizing, and proposed many algorithms and proposals. Many products were put into practical use, but they never achieved the performance as people had expected. There are many works need to be done to improve the accuracy of plate locating and recognizing, robustness and real-time ability.
On the basis of other people’s research, this paper accomplished research and design of vehicle LPR system. This paper’s theory foundation is the new theory——wavelet analysis. The system consists of three main modules: (1) Vehicle license plate extraction; (2) Character segmentation; (3) Character recognition.
In plate extraction module, this paper employs wavelet analysis to extract the number plate from the complicated background image. The algorithm locates the plate correctly based on wavelet analysis. According to the experiment, the algorithm achieves the goal and the rate of plate location of this system is over 98%.
In the character segmentation module, area growth algorithm is adopted, and plate image vertical projection is combined to segment the character. The algorithm is much better than the traditional methods based on projection. In the character recognition module, the traditional character recognition method based on neural network is adopted in this paper. But the BP neural network has been modified a little and the performance of the BP neural network is improved.
引文
[1].Rafael C.Gonzalez, Richard E.Woods, Digital Image Processing (Prentice-Hall Inc,2002).
[2].Comelli, P.Ferragina, P.Granieri, M.N.Stabile, Optical recognition of motor vehicle license plates, Vehicular Technology, IEEE Transactions in Nov 1995, 790–799
[3].Shyang-Lih Chang, Li-Shien Chen, Yun-Chung Chung, Sei-Wan Chen, Automatic license plate recognition. Intelligent Transportation Systems, IEEE Transactions on Volume 5, Issue 1, March 2004 Page(s):42 - 53.
[4].Aghdasi F,Ndungo H, Automatic licence plate recognition system, AFRICON, 2004. 7th AFRICON Conference in Africa Volume 1, 15-17 Sept. 2004 Page(s):45 - 50 Vol.1.
[5].Sunghoom Kim, Daechul Kim, Younbok Ryu, Gyeonghwan Kim. A robust license-plate extraction method under complex image conditions, Pattern Recognition, 2002. Proceedings. 16th International Conference, 11-15 Aug. 2002 , 216 - 219 vol.3
[6].Taleb-Ahmed, A.Hamad, D.Tilmant. Vehicle license plate recognition in marketing application. Intelligent Vehicles Symposium. IEEE, 9-11 June 2003 Pages:90 - 94
[7].Yoshimori, S. Mitsukura, Y. Fukumi, M. Akamatsu, N. Khosal, R. License plate detection system in rainy days, Computational Intelligence in Robotics and Automation, 16-20 July 2003, 972 - 976 vol.2
[8].Chacon M, M.I. Zimmerman, A License plate location based on a dynamic PCNN scheme Neural Networks. Proceedings of the International Joint Conference, 20-24July 2003 ,1195 - 1200 vol.2
[9].Nason, G.P. A little introduction to wavelets. Applied Statistical Pattern Recognition, IEEE Colloquium on, 20 April 1999, 1/1 - 1/6.
[10].刘效静、成瑜,汽车牌照自动识别技术研究,南京航空航天大学学报,1998,30(5)
[11].廖金周,宣国荣,车辆牌照的自动分割,微型电脑应用,1999,7。
[12].刘智勇,刘迎建,车牌识别中的图像提取及分割,微型电脑应用,1999,7。
[13].王昱,赵正校,一种基于样本线搜索和霍夫变换的区域提取方法,计算机工程,1999,25(9)。
[14].史湘全,何菀凌,蔡孟波,遗传算法在车牌提取中的应用,公路交通科技,2000,17(5)。
[15].ERLEE, PKKIM, HJ.Kiutomatic recognition of a car license plate using color image processing[A],Proc of Internation Conferenceon Image Processing ,1994,12:301-305.
[16].张引,潘云鹤,彩色汽车图像牌照提取新方法,上海交通大学学报,1998,32(10)。
[17].张云刚,张长水,利用 Hough 变换和先验知识的车牌字符分割算法,计算机学报,2004.1。
[18].骆雪超、刘桂雄、冯云庆、申柏华,一种基于车牌特征信息的车牌识别方法,华南理工大学学报,2003.4。
[19].崔江,王友仁,车牌自动识别方法中的关键技术研究,计算机测量与控制,2003,11(4)。
[20].程正兴,小波分析算法与应用,西安交通大学出版社。
[21].小波理论及其在图像处理中的应用,测绘学报,1998 年 03 期。
[22].小波变换及其在图像处理中的应用,四川大学学报(自然科学版),1994 年 04 期。
[23].Dong-June Lee, Seong-Whan Lee, A new methodology for gray-scale character segmentation and recognition. Analysis and Recognition, 1995.
[24].Byung Tae Chun, Younglae Bae, Tai-Yun Kim,Text extraction in videos using topographical features of characters,Fuzzy Systems Conference Proceedings, 1999.FUZZ-IEEE '99. 1999 IEEE International Volume 2,22-25 Aug.
[25].TMS320C6000 Optimizing C/C++ Compiler User’s Guide, Texas Instruments Incorporated, April 2001.
[26].Chutatape, Li Qian Xiaodong, Automatic license number extraction and its parallel implementation, Industrial Electronics Society, 1999, IECON '99.
[27].Fahmy, M.M.M., Automatic number-plate recognition: neural network approach, Vehicle Navigation and Information Systems Conference, 1994.
[28].Da Rocha Gesualdi, A., Manoel de Seixas, J. Character recognition in car license plates based on principal components and neural processing, Neural Networks, 2002. SBRN 2002.
[29].Shen-Zheng Wang; Hsi-Jian Lee, Detection and recognition of license plate characters with different appearances, Intelligent Transportation Systems, 2003.
[30].Sirithinaphong, T.; Chamnongthai, K., Extraction of car license plate using motor vehicle regulation and character pattern recognition, Circuits and Systems, 1998. IEEE APCCAS 1998.
[31].Daliang Liu, Mei Xie, Detection of License Plate Based on Wavelet Transform, Circuits, Singnals, and Systems, CSS 2004, November 28 - December 1, 2004, Clearwater Beach, Florida, USA.
[32].In-Jung Kim, Jin-Hyung Kim. Statistical Character Structure Modeling and Its Application to Handwritten Chinese Character Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 25 NO 11, November 2003
[2].Comelli, P.Ferragina, P.Granieri, M.N.Stabile, Optical recognition of motor vehicle license plates, Vehicular Technology, IEEE Transactions in Nov 1995, 790–799
[3].Shyang-Lih Chang, Li-Shien Chen, Yun-Chung Chung, Sei-Wan Chen, Automatic license plate recognition. Intelligent Transportation Systems, IEEE Transactions on Volume 5, Issue 1, March 2004 Page(s):42 - 53.
[4].Aghdasi F,Ndungo H, Automatic licence plate recognition system, AFRICON, 2004. 7th AFRICON Conference in Africa Volume 1, 15-17 Sept. 2004 Page(s):45 - 50 Vol.1.
[5].Sunghoom Kim, Daechul Kim, Younbok Ryu, Gyeonghwan Kim. A robust license-plate extraction method under complex image conditions, Pattern Recognition, 2002. Proceedings. 16th International Conference, 11-15 Aug. 2002 , 216 - 219 vol.3
[6].Taleb-Ahmed, A.Hamad, D.Tilmant. Vehicle license plate recognition in marketing application. Intelligent Vehicles Symposium. IEEE, 9-11 June 2003 Pages:90 - 94
[7].Yoshimori, S. Mitsukura, Y. Fukumi, M. Akamatsu, N. Khosal, R. License plate detection system in rainy days, Computational Intelligence in Robotics and Automation, 16-20 July 2003, 972 - 976 vol.2
[8].Chacon M, M.I. Zimmerman, A License plate location based on a dynamic PCNN scheme Neural Networks. Proceedings of the International Joint Conference, 20-24July 2003 ,1195 - 1200 vol.2
[9].Nason, G.P. A little introduction to wavelets. Applied Statistical Pattern Recognition, IEEE Colloquium on, 20 April 1999, 1/1 - 1/6.
[10].刘效静、成瑜,汽车牌照自动识别技术研究,南京航空航天大学学报,1998,30(5)
[11].廖金周,宣国荣,车辆牌照的自动分割,微型电脑应用,1999,7。
[12].刘智勇,刘迎建,车牌识别中的图像提取及分割,微型电脑应用,1999,7。
[13].王昱,赵正校,一种基于样本线搜索和霍夫变换的区域提取方法,计算机工程,1999,25(9)。
[14].史湘全,何菀凌,蔡孟波,遗传算法在车牌提取中的应用,公路交通科技,2000,17(5)。
[15].ERLEE, PKKIM, HJ.Kiutomatic recognition of a car license plate using color image processing[A],Proc of Internation Conferenceon Image Processing ,1994,12:301-305.
[16].张引,潘云鹤,彩色汽车图像牌照提取新方法,上海交通大学学报,1998,32(10)。
[17].张云刚,张长水,利用 Hough 变换和先验知识的车牌字符分割算法,计算机学报,2004.1。
[18].骆雪超、刘桂雄、冯云庆、申柏华,一种基于车牌特征信息的车牌识别方法,华南理工大学学报,2003.4。
[19].崔江,王友仁,车牌自动识别方法中的关键技术研究,计算机测量与控制,2003,11(4)。
[20].程正兴,小波分析算法与应用,西安交通大学出版社。
[21].小波理论及其在图像处理中的应用,测绘学报,1998 年 03 期。
[22].小波变换及其在图像处理中的应用,四川大学学报(自然科学版),1994 年 04 期。
[23].Dong-June Lee, Seong-Whan Lee, A new methodology for gray-scale character segmentation and recognition. Analysis and Recognition, 1995.
[24].Byung Tae Chun, Younglae Bae, Tai-Yun Kim,Text extraction in videos using topographical features of characters,Fuzzy Systems Conference Proceedings, 1999.FUZZ-IEEE '99. 1999 IEEE International Volume 2,22-25 Aug.
[25].TMS320C6000 Optimizing C/C++ Compiler User’s Guide, Texas Instruments Incorporated, April 2001.
[26].Chutatape, Li Qian Xiaodong, Automatic license number extraction and its parallel implementation, Industrial Electronics Society, 1999, IECON '99.
[27].Fahmy, M.M.M., Automatic number-plate recognition: neural network approach, Vehicle Navigation and Information Systems Conference, 1994.
[28].Da Rocha Gesualdi, A., Manoel de Seixas, J. Character recognition in car license plates based on principal components and neural processing, Neural Networks, 2002. SBRN 2002.
[29].Shen-Zheng Wang; Hsi-Jian Lee, Detection and recognition of license plate characters with different appearances, Intelligent Transportation Systems, 2003.
[30].Sirithinaphong, T.; Chamnongthai, K., Extraction of car license plate using motor vehicle regulation and character pattern recognition, Circuits and Systems, 1998. IEEE APCCAS 1998.
[31].Daliang Liu, Mei Xie, Detection of License Plate Based on Wavelet Transform, Circuits, Singnals, and Systems, CSS 2004, November 28 - December 1, 2004, Clearwater Beach, Florida, USA.
[32].In-Jung Kim, Jin-Hyung Kim. Statistical Character Structure Modeling and Its Application to Handwritten Chinese Character Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 25 NO 11, November 2003