复杂背景下车牌分割技术研究
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摘要
随着经济的发展,汽车数量的增加,公路交通成为我国重要的交通运输途径。日益拥堵的城市交通需要更先进、更有效的交通管理、控制。利用电子信息技术来提高管理效率、交通效率和安全的智能交通系统(ITS)已经成为当前交通管理发展的主要方向。车牌识别系统(LPR)是智能交通系统(ITS)的核心组成部分。它以自动的车牌号码识别为基础,可以对车辆进行自动登记、验证、监视、报警,进而可以应用在多种场合,如高速公路收费系统;道路、卡口监控系统;小区、停车场收费、监控系统;车流统计、引导系统等。
近年来,使用计算机来对图像进行处理和分析,已经获得了飞速的发展。对车牌识别系统中关键技术的研究已经成为科学界的一个热点问题。
车牌定位,车牌字符分割,车牌字符识别是车牌识别的三个重要组成部分。本文从车牌定位及车牌字符分割,对车牌识别技术进行了比较深入、全面的论述,并对主要部分的关键技术进行了深入的研究。
本文采用的是一种综合的车牌分割方法。该方法充分利用了车牌的纹理特征和几何特征。实验所采用的图像均是采集于各种真实的环境条件下,一般来说,采用的图像背景较为复杂,去除背景干扰后再进行车牌定位。车牌粗定位中先用投影法粗步确定上下边界,再用扫描线法从候选车牌区域的中间位置向上下扫描统计跳变数精确定位上下边界。根据车牌长宽比使用滑动窗口确定车牌左右边界。
在字符切分中,对图像二值化处理后,进行倾斜矫正,运用数学形态学运算消除噪音填补空洞,在此基础上用H-S连通域法提取字符,最后利用投影分割出单个字符的准确位置。投影法确定字符边界时,考虑到‘川’这样的字符,本文提出新的由投影确定字符竖直边界的方法。
实验结果表明,本文所采用的方法能达到较好的分割效果,具有一定的鲁棒性和实时性。
With the development of the economy and the increase in the number of vehicles, the highway communication becomes an important means of transportation. Also crowded urban traffic needs more advanced and more effective traffic administration and control system. Intelligent Transportation System(ITS) which makes use of electronic information technology to raise management efficiency, traffic efficiency and traffic security becomes main direction of traffic administration. License Plate Recognition System (LPR) is the core of Intelligent Transportation System (ITS). The system can automatically register, verify, monitor vehicle or report to the police with automatic recognition for vehicle license plates. So it can be used in many kinds of occasions, such as the charges system of expressway, monitoring system at road and roll-gate, charge and monitoring system at the district, parking area, guide system, the system counting the quantity of vehicle passing in a certain period time, and so on.
In recent years, using the computer to deal with and analyze image has already made remarkable progress. The study on the critical techniques for LPR has already became an important research field in scientific community.
License plate location, license plate character segmentation, license plate recognition are the three essential components of the License Plate Recognition System. In this paper we discussed the tow parts of License Plate Recognition System: license plate location and character segmentation, and made a deep research about some important and key technology.
A synthetic license plate segmentation scheme is presented in this paper. This algorithm makes full use of the texture and geometric characteristics of the license plate .The mainly stages of this approach are designed to deal with images taken under various real world conditions. Generally, the images have complex background. First we wipe off the complex scenes and then we carry on the license localization. In this step the image projection method is used to determine the horizontal boundary roughly first, then the line-scanning method is implemented to count the jump variables to locate horizontal boundary accurately. Next, the sliding window is adopted to designate vertical boundary according to the ratio of the length and width of the plate.
In the character segmentation stage, after binarizing the image of license plate and correcting the tilt, the Mathematical Morphology operation is applied to the image processing to eliminate the noise and to fill the hollows. After extracting the characters of license plate with the H-S method, we segment each character through projection.In view of the character such as‘川’,this article presents a new method to determine the vertical border of the character according to the projection.
The experimental results indicate that the proposed method can effectively locate license plate under complex background and segment characters correctly. The performance of the system is promising.
近年来,使用计算机来对图像进行处理和分析,已经获得了飞速的发展。对车牌识别系统中关键技术的研究已经成为科学界的一个热点问题。
车牌定位,车牌字符分割,车牌字符识别是车牌识别的三个重要组成部分。本文从车牌定位及车牌字符分割,对车牌识别技术进行了比较深入、全面的论述,并对主要部分的关键技术进行了深入的研究。
本文采用的是一种综合的车牌分割方法。该方法充分利用了车牌的纹理特征和几何特征。实验所采用的图像均是采集于各种真实的环境条件下,一般来说,采用的图像背景较为复杂,去除背景干扰后再进行车牌定位。车牌粗定位中先用投影法粗步确定上下边界,再用扫描线法从候选车牌区域的中间位置向上下扫描统计跳变数精确定位上下边界。根据车牌长宽比使用滑动窗口确定车牌左右边界。
在字符切分中,对图像二值化处理后,进行倾斜矫正,运用数学形态学运算消除噪音填补空洞,在此基础上用H-S连通域法提取字符,最后利用投影分割出单个字符的准确位置。投影法确定字符边界时,考虑到‘川’这样的字符,本文提出新的由投影确定字符竖直边界的方法。
实验结果表明,本文所采用的方法能达到较好的分割效果,具有一定的鲁棒性和实时性。
With the development of the economy and the increase in the number of vehicles, the highway communication becomes an important means of transportation. Also crowded urban traffic needs more advanced and more effective traffic administration and control system. Intelligent Transportation System(ITS) which makes use of electronic information technology to raise management efficiency, traffic efficiency and traffic security becomes main direction of traffic administration. License Plate Recognition System (LPR) is the core of Intelligent Transportation System (ITS). The system can automatically register, verify, monitor vehicle or report to the police with automatic recognition for vehicle license plates. So it can be used in many kinds of occasions, such as the charges system of expressway, monitoring system at road and roll-gate, charge and monitoring system at the district, parking area, guide system, the system counting the quantity of vehicle passing in a certain period time, and so on.
In recent years, using the computer to deal with and analyze image has already made remarkable progress. The study on the critical techniques for LPR has already became an important research field in scientific community.
License plate location, license plate character segmentation, license plate recognition are the three essential components of the License Plate Recognition System. In this paper we discussed the tow parts of License Plate Recognition System: license plate location and character segmentation, and made a deep research about some important and key technology.
A synthetic license plate segmentation scheme is presented in this paper. This algorithm makes full use of the texture and geometric characteristics of the license plate .The mainly stages of this approach are designed to deal with images taken under various real world conditions. Generally, the images have complex background. First we wipe off the complex scenes and then we carry on the license localization. In this step the image projection method is used to determine the horizontal boundary roughly first, then the line-scanning method is implemented to count the jump variables to locate horizontal boundary accurately. Next, the sliding window is adopted to designate vertical boundary according to the ratio of the length and width of the plate.
In the character segmentation stage, after binarizing the image of license plate and correcting the tilt, the Mathematical Morphology operation is applied to the image processing to eliminate the noise and to fill the hollows. After extracting the characters of license plate with the H-S method, we segment each character through projection.In view of the character such as‘川’,this article presents a new method to determine the vertical border of the character according to the projection.
The experimental results indicate that the proposed method can effectively locate license plate under complex background and segment characters correctly. The performance of the system is promising.
引文
[1].陆化普,李瑞敏,朱茵.智能交通系统概述[M].北京:中国铁道出版社,2004.
[2]. Mei Yu,Yong Deak Kim. An approach to Korean license plate recognition based on vertical edge matching[J]. IEEE International Conference on Systems,Man and Cybernetics,2000, 4:2975-2980.
[3].Shyan-Lih Chant, Li-Shien Chen, Yun-Chung Chung, Sei-Wan Chen. Automatic license plate recognition[J]. IEEE Transactions on intelligent Transportation Systems,2004,5(1):42-53.
[4].刘智勇,刘迎建.车牌识别中的图像提取及分割[J].微型电脑应用,1999,7:45-48.
[5].何斌,马天予,王运坚,朱红莲.Visual C++数字图像处理[M].北京:人民邮电出版社,2002.
[6].任明武.数字图像处理[M].南京:南京理工大学出版社,1997.
[7].Huang T S,Tang G T.A fast two-dimensional median filtering algorithm[J]. IEEE Trans Acoustics,Speech, and Signal Processing, 1979,27(1):1318.
[8].冯国进,顾国华,郑瑞红.基于自适应投影方法的快速车牌定位[J].红外与激光程,2003,32(2):285-288.
[9].刘效静,成瑜.汽车牌照自动识别技术研究[J].南京航空航天大学学报,1998,30(5):12-17.
[10].牛欣,沈兰荪.基于特征的车辆牌照定位算法[J].交通与计算机,2000,(1):31-33.
[11].廖金周,宣国荣.车辆牌照的自动分割[J].微型电脑应用,1999,(7):32-34.
[12].张玉姣,史忠科.车牌提取方法研究[J].西北工业大学学报,2003,(2):34-37.
[13].王昱,赵正校等.一种基于样本线搜索和霍夫变换的区域定位方法[J].红外线与激光工程,1999.28(4):128-132.
[14].袋青云,余英林.一种基于小波与形态学的车牌图像分割方法[J].中国图像图形学报, 2005,5(5):411-415.
[15].江萍,徐晓冰,方敏.基于Top-hat变换的车牌快速搜索与定位[J].合肥工业大学学报,2003,26(3):336-340.
[16].白洪亮,娄正良,邹明福,刘昌平.复杂背景下基于形态学的车牌识别系统[J].公路交通科技.2004,21(10):117-120.
[17].韩智广等.车牌分割与矫正[J].计算机工程与应用,2003,(9):210-212.
[18].是湘全,何苑凌等.遗传算法在车牌定位中的应用[J].公路交通科技,2000,17(2):33-36.
[19].李文举,梁德群,张旗,樊鑫.基于边缘颜色对的车牌定位新方法[J].计算机学报,2004,27(2):204-208.
[20].张引,潘云鹤.彩色汽车图像牌照定位新方法[J].中国图形图像学报,2001,6(4):32-38.
[21]XifanShi,WeizhongZhao, YonghangShen. Automatic license plate recognition system based on color image Processing[C].ICCSA 2005, LNCS 3483,PP.1159-1168,2005.
[22].赵雪春,戚飞虎.基于彩色分割的车牌自动识别技术[J].上海交通大学学报,1998, 32(10):24-27.
[23].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[J].计算机学报,2004,(1):149-153.
[24].陈寅鹏,丁晓清.复杂车辆图像中的车牌定位与字符分割方法[J].红外与激光工程, 2004,33(1):29-33.
[25].袁志伟,潘晓露,陈艾,李一民.车辆牌照定位的算法研究[J].昆明理工大学学报,2001,(4):56-60.
[26].李文举,梁德群.质量退化的车牌字符分割方法[J].计算机辅助设计与图形学报,2004 ,6(5) :697 -700.
[27].H.Goto, H.Aso. Character Pattern Extraction from Colorful Documents with Complex Backgrounds[C].Proceedings of 16th International Conference on Pattern Recognition, ICPR2002, Aug. 2002,Canada.
[28].陈锻生,谢志鹏,刘政凯.复杂背景下彩色图像车牌提取与字符分割技术[J].小型微型计算机系统,2002,(9):1144-1148.
[29].Ostu N. A threshold selection method from gray-level histogram [J]. IEEE Trans Systems Man Cybernetic,1978, (8):62-65.
[30].Dong-JuneLee,Seong-WhanLee. A new Methodology for Gray-Scale Character Segmentation and Recognition [J],IEEE Trans on Pattern Analysis and Machine Intelligence,1996,18 (10):526-527.
[31].李填,夏良正.一种新的二维最大熵图像阈值分割方法[J].南京航空航天大学学报,1994, 12(1):104-106.
[32].Bemsen J. Dynamic thresholding of gray-level images[A]. Proc. 8th Int. Conf. on Pattern Recognition [C].Los Angeles: IEEE Computer Society Press, 1986:1251-1255.
[33].戴青云,余英林.数学形态学在图像处理中的应用进展[J].控制理论与应用,2001,18(4):478-482.
[34].Stromberg J O.A modified Franklin system and higher order spline systems on as unconditional bases for Hardy spaces[C]. Beckner W.Conf in honor of A Zygmund.New York:Academic Press,1986,475-493.
[35].Haralic R M , Shapiro L G. Computer and Robot Vision [M ]. New York: Addision-wesley, 1992. 105- 120.
[36].左望霞.复杂背景下多车牌图像分割技术研究[D].电子科技大学硕士学位论文,2006.
[37].张二虎等.FA中字符分割提取算法的研究[J].计算机工程与应用,2000,(9):82-83.
[2]. Mei Yu,Yong Deak Kim. An approach to Korean license plate recognition based on vertical edge matching[J]. IEEE International Conference on Systems,Man and Cybernetics,2000, 4:2975-2980.
[3].Shyan-Lih Chant, Li-Shien Chen, Yun-Chung Chung, Sei-Wan Chen. Automatic license plate recognition[J]. IEEE Transactions on intelligent Transportation Systems,2004,5(1):42-53.
[4].刘智勇,刘迎建.车牌识别中的图像提取及分割[J].微型电脑应用,1999,7:45-48.
[5].何斌,马天予,王运坚,朱红莲.Visual C++数字图像处理[M].北京:人民邮电出版社,2002.
[6].任明武.数字图像处理[M].南京:南京理工大学出版社,1997.
[7].Huang T S,Tang G T.A fast two-dimensional median filtering algorithm[J]. IEEE Trans Acoustics,Speech, and Signal Processing, 1979,27(1):1318.
[8].冯国进,顾国华,郑瑞红.基于自适应投影方法的快速车牌定位[J].红外与激光程,2003,32(2):285-288.
[9].刘效静,成瑜.汽车牌照自动识别技术研究[J].南京航空航天大学学报,1998,30(5):12-17.
[10].牛欣,沈兰荪.基于特征的车辆牌照定位算法[J].交通与计算机,2000,(1):31-33.
[11].廖金周,宣国荣.车辆牌照的自动分割[J].微型电脑应用,1999,(7):32-34.
[12].张玉姣,史忠科.车牌提取方法研究[J].西北工业大学学报,2003,(2):34-37.
[13].王昱,赵正校等.一种基于样本线搜索和霍夫变换的区域定位方法[J].红外线与激光工程,1999.28(4):128-132.
[14].袋青云,余英林.一种基于小波与形态学的车牌图像分割方法[J].中国图像图形学报, 2005,5(5):411-415.
[15].江萍,徐晓冰,方敏.基于Top-hat变换的车牌快速搜索与定位[J].合肥工业大学学报,2003,26(3):336-340.
[16].白洪亮,娄正良,邹明福,刘昌平.复杂背景下基于形态学的车牌识别系统[J].公路交通科技.2004,21(10):117-120.
[17].韩智广等.车牌分割与矫正[J].计算机工程与应用,2003,(9):210-212.
[18].是湘全,何苑凌等.遗传算法在车牌定位中的应用[J].公路交通科技,2000,17(2):33-36.
[19].李文举,梁德群,张旗,樊鑫.基于边缘颜色对的车牌定位新方法[J].计算机学报,2004,27(2):204-208.
[20].张引,潘云鹤.彩色汽车图像牌照定位新方法[J].中国图形图像学报,2001,6(4):32-38.
[21]XifanShi,WeizhongZhao, YonghangShen. Automatic license plate recognition system based on color image Processing[C].ICCSA 2005, LNCS 3483,PP.1159-1168,2005.
[22].赵雪春,戚飞虎.基于彩色分割的车牌自动识别技术[J].上海交通大学学报,1998, 32(10):24-27.
[23].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[J].计算机学报,2004,(1):149-153.
[24].陈寅鹏,丁晓清.复杂车辆图像中的车牌定位与字符分割方法[J].红外与激光工程, 2004,33(1):29-33.
[25].袁志伟,潘晓露,陈艾,李一民.车辆牌照定位的算法研究[J].昆明理工大学学报,2001,(4):56-60.
[26].李文举,梁德群.质量退化的车牌字符分割方法[J].计算机辅助设计与图形学报,2004 ,6(5) :697 -700.
[27].H.Goto, H.Aso. Character Pattern Extraction from Colorful Documents with Complex Backgrounds[C].Proceedings of 16th International Conference on Pattern Recognition, ICPR2002, Aug. 2002,Canada.
[28].陈锻生,谢志鹏,刘政凯.复杂背景下彩色图像车牌提取与字符分割技术[J].小型微型计算机系统,2002,(9):1144-1148.
[29].Ostu N. A threshold selection method from gray-level histogram [J]. IEEE Trans Systems Man Cybernetic,1978, (8):62-65.
[30].Dong-JuneLee,Seong-WhanLee. A new Methodology for Gray-Scale Character Segmentation and Recognition [J],IEEE Trans on Pattern Analysis and Machine Intelligence,1996,18 (10):526-527.
[31].李填,夏良正.一种新的二维最大熵图像阈值分割方法[J].南京航空航天大学学报,1994, 12(1):104-106.
[32].Bemsen J. Dynamic thresholding of gray-level images[A]. Proc. 8th Int. Conf. on Pattern Recognition [C].Los Angeles: IEEE Computer Society Press, 1986:1251-1255.
[33].戴青云,余英林.数学形态学在图像处理中的应用进展[J].控制理论与应用,2001,18(4):478-482.
[34].Stromberg J O.A modified Franklin system and higher order spline systems on as unconditional bases for Hardy spaces[C]. Beckner W.Conf in honor of A Zygmund.New York:Academic Press,1986,475-493.
[35].Haralic R M , Shapiro L G. Computer and Robot Vision [M ]. New York: Addision-wesley, 1992. 105- 120.
[36].左望霞.复杂背景下多车牌图像分割技术研究[D].电子科技大学硕士学位论文,2006.
[37].张二虎等.FA中字符分割提取算法的研究[J].计算机工程与应用,2000,(9):82-83.