A Real-Time Mobile Vehicle License Plate Detection and Recognition

Kuo-Ming Hung; Ching-Tang Hsieh

doi:10.6180/jase.2010.13.4.09

A Real-Time Mobile Vehicle License Plate Detection and Recognition

Kuo-Ming Hung This email address is being protected from spambots. You need JavaScript enabled to view it.^1,2 and Ching-Tang Hsieh²

¹Department of Information Management, Kainan University, Taoyuan, Taiwan 338, R.O.C.
²Department of Electrical Engineering, Tamkang University, Tamsui, Taiwan 251, R.O.C.

Received: December 3, 2008
Accepted: September 7, 2009
Publication Date: December 1, 2010

Download Citation: ||https://doi.org/10.6180/jase.2010.13.4.09

ABSTRACT

In this paper we present a instant and real-time mobile vehicle license plate recognition system in an open environment. Using a nonfixed video camera installed in the car, the system tries to capture the image of the car in front and to process instant vehicle license plate detection and recognition. We utilize the color characteristics of the barking lights to carry out license plate detection. We first detect the location of the two barking lights in the captured image. Then set license plate detection region using the probability distribution of the license plate between the two lights. This method can eliminate any environmental interference during the license plate detection and improve the rate of accuracy of license plate detection and recognition. Moreover, we use the morphology method Black Top-Hat to enhance the level of separation of the license plate characters. Experiments show that the system can effectively and quickly capture the vehicle image, detect and recognize the license plate whether it is in daytime, nighttime, clear day, raining day or under complicated environment.

Keywords: Real-Time, Wavelet, License Plate, Black Top-Hat

REFERENCES

[1]Kuo-Ming Hung, Hsiang-Lin Chuang and Ching-Tang Hsieh,” License Plate Detection Based on Expanded Haar Wavelet transform”, 4th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD'07), August 2007,Haikou, China,pp.415-419.
[2]T. Hsieh, Y. S. Juan, and K. M. Hung, “Multiple license plate detection for complex background,” in Proc. IEEE 19th Int. Conf. AINA, 2005,pp. 389–392.
[3]Freund, “An adaptive version of the boost by majority algorithm,” Machine Learning, 43(3), 2001, pp. 293-318.
[4]G. Zhu, G. J. Hou, X. Jia; “A study of locating vehicle license plate based on color feature and mathematical morphology.” in Proc. Int. Conf. Signal Processing, Aug. 2002, pp. 748-751.
[5]B. Huang, and Y. F. Guo, “Classifier Fusion-Based Vehicle License Plate Detection Algorithm,“ in IEEE Proc. Int. Conf. Machine Learning and Cybernetics, vol. 5, Nov. 2003, pp. 2984-2989.
[6]W. Park, S. C. Hwang, and J. W. Park, “The Extraction of Vehicle License Plate Region Using Edge Directional Properties of Wavelet Subband,” IEICE Trans. on Information & System, vol. E86-D, no. 3, March 2003, pp. 664-669.
[7]Naito, T. Tsukada, K. Yamada, K. Kozuka, and S. Yamamoto, “Robust License-Plate Recognition Method for Passing Vehicles under outside Environment,” IEEE Trans. on Vehicular Technology, vol. 49, no. 6, Nov. 2000, pp. 2309-2319.
[8]Q. Yang, J. Bai, R. L. Tian, and N. Liu, “A vehicle license plate recognition system based on fixed color collocation” in Proc. IEEE 4th Int. Conf. Mach. Learn. Cybern., Guangzhou, China, 2005,pp. 5394–5397.
[9]Porikli and T. Kocak, “Robust license plate detection using covariance descriptor in a neural network framework,” in Proc. IEEE Adv. Video Signal Based Surveillance Conf., 2006, pp. 107.
[10]Jing-Ming Guo, Yun-Fu Liu,” License plate localization and character segmentation with feedback self-learning and hybrid binarization techniques”, Vehicular Technology, IEEE Transactions on Volume 57, Issue 3, May 2008 pp.1417 – 1424.
[11]Rahman A., Badawy W., Radmanesh A., “A real time vehicle's license plate recognition system.”, Proceedings. IEEE Conference on Advanced Video and Signal Based Surveillance, 2003, pp. 163 – 166.
[12]Hsi-Jian Lee, Si-Yuan Chen and Shen-Zheng Wang, “Extraction and recognition of license plates of motorcycles and vehicles on highways”, 17th International Conference on Pattern Recognition( ICPR 2004), 2004 , Vol.4,pp.356 – 359.
[13]C. Gonzalez and R. E. Woods, Digital Image Processing. Reading,MA: Addison-Wesley, 1993.
[14]Rafael C. Gonzalez, Richard E. Woods, Digital Image Processing, Prentice Hall, 2008.
[15]K. Pratt, Digital Image Processing. Hoboken, NJ: Wiley, 1991.
[16]Martin, M. Garcia, and J. L. Alba, “New methods for automatic reading of VLP’s (Vehicle License Plates),” in Proc. IASTED Int. Conf. SPPRA, 2002.
[17]Wu, L. C. On, C. H. Weng, T. S. Kuan, and K. Ng, “A Macao license plate recognition system,” in Proc. 4th Int. Conf. Mach. Learn. Cybern., Guangzhou, China, Aug. 18–21, 2005, pp. 4506–4510.