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License Plate Recognition System Using Artificial Neural Networks

  • Received : 2015.08.26
  • Accepted : 2016.06.07
  • Published : 2017.04.01

Abstract

A high performance license plate recognition system (LPRS) is proposed in this work. The proposed LPRS is composed of the following three main stages: (i) plate region determination, (ii) character segmentation, and (iii) character recognition. During the plate region determination stage, the image is enhanced by image processing algorithms to increase system performance. The rectangular license plate region is obtained using edge-based image processing methods on the binarized image. With the help of skew correction, the plate region is prepared for the character segmentation stage. Characters are separated from each other using vertical projections on the plate region. Segmented characters are prepared for the character recognition stage by a thinning process. At the character recognition stage, a three-layer feedforward artificial neural network using a backpropagation learning algorithm is constructed and the characters are determined.

Keywords

References

  1. C.N.E. Anagnostopoulos et al., "License Plate Recognition from Still Images and Video Sequence: a Survey," IEEE Trans. Intell. Trans. Syst., vol. 9, no. 3, Sept. 2008, pp. 377-391. https://doi.org/10.1109/TITS.2008.922938
  2. C.N.E. Anagnostopoulos et al., "A License Plate-Recognition Algorithm for Intelligent Transportation System Applications," IEEE Trans. Intell. Transp. Syst., vol. 7, no 3, Sept. 2006, pp. 377-392. https://doi.org/10.1109/TITS.2006.880641
  3. X. Shi, W. Zhao, and Y. Shen, "Automatic License Plate Recognition System Based on Color Image Processing," Comput. Sci. Its Applicat., Singapore, May 9-12, 2005, pp. 1159-1168.
  4. N. Zimic et al., "The Fuzzy Logic Approach to the Car Number Plate Locating Problem," Proc. Intell. Inform. Syst., Bahamas, Dec. 8-10, 1997, pp. 227-230.
  5. R. Zunino and S. Rovetta, "Vector Quantization for License-Plate Location and Image Coding," IEEE Trans. Ind. Electron., vol. 47, no. 1, Feb. 2000, pp. 159-167. https://doi.org/10.1109/41.824138
  6. T.D. Duan et al., "Building an Automatic Vehicle License-Plate Recognition System," Proc. Int. Conf. Comput. Sci., Cantho, Vietnam, Feb. 2005, pp. 59-63.
  7. F. Kahraman, B. Kurt, and M. Gokmen, "License Plate Character Segmentation Based on the Gabor Transform and Vector Quantization," ISCIS Comput. Inform. Sci., vol. 2869, 2003, pp. 381-388.
  8. C.T. Hsieh, Y.S. Juan, and K.M. Hung, "Multiple License Plate Detection for Complex Background," Int Conf. Adv. Inform. Netw. Aapplcat., Taipei, Taiwan, Mar. 28-30, 2005, pp. 389-392.
  9. L. Dlagnekovin, Video-Based Car Surveillance: License Plate, Make, and Model Recognition, M.S. thesis, Comput. Sci. Eng. Dept., Univ. California, San Diego, USA, 2004.
  10. L. Gang, Z. Ruili, and L. Ling, "Research on Vehicle License Plate Location Based on Neural Networks," Int. Conf. Innovative Comput., Inform. Contr., Beijing, China, Aug. 30-Sept. 1, 2006, pp. 174-177.
  11. J.A.G. Nijhuis et al., "Car License Plate Recognition with Neural Networks and Fuzzy Logic," Proc. IEEE Int. Conf. Neural Netw., Perth, Australia, Nov. 27-Dec. 1, 1995, pp. 2232-2236.
  12. S.K. Kim, D.W. Kim, and H.J. Kim, "A Recognition of Vehicle License Plate Using a Genetic Algorithm Based Segmentation," Proc. Int. Conf. Image Process., Sept. 1996, pp. 661-664.
  13. J. Xiong et al., "Locating Car License Plate Under Various Illumination Conditions Using Genetic Algorithm," Proc. Int. Conf. Signal Process., Beijing, China, Aug. 31-Sept. 4, 2004, pp. 2502-2505.
  14. B. Hongliang and L. Changping, "A Hybrid License Plate Extraction Method Based on Edge Statistics and Morphology," Proc. Int. Conf. Pattern Recogn., Cambridge, UK, Aug. 23-26, 2004, pp. 831-834.
  15. D. Zheng, Y. Zhao, and J. Wang, "An Efficient Method of License Plate Location," Pattern Recogn. Lett., vol. 26, no. 15, Nov. 2005, pp. 2431-2438. https://doi.org/10.1016/j.patrec.2005.04.014
  16. S. Nomura et al., "A Novel Adaptive Morphological Approach for Degraded Character Image Segmentation," Pattern Recogn., vol. 38, no. 11, Nov. 2005, pp. 1961-1975. https://doi.org/10.1016/j.patcog.2005.01.026
  17. A. Capar and M. Gokmen, "Concurrent Segmentation and Recognition with Shape-Driven Fast Marching Methods," Proc. Int. Conf. Pattern Recogn., Hong Kong, China, Aug. 20-24, 2006, pp. 155-158.
  18. B.G. Han et al., "Real-Time License Plate Detection in High-Resolution Videos Using Fastest Available Cascade Classifier and Core Patterns," ETRI J., vol. 37, no. 2, Apr. 2015, pp. 251-261. https://doi.org/10.4218/etrij.15.2314.0077
  19. Y. Yoon et al., "Best Combination of Binarization Methods for License Plate Character Segmentation," ETRI J., vol. 35, no. 3, June 2013, pp. 491-500. https://doi.org/10.4218/etrij.13.0112.0545
  20. K. Sonavane, B. Soni, and U. Majhi, "Survey on Automatic Number Plate Recognition," Int. J. Comput. Applicat., vol. 125, no. 6, 2015, pp. 1-4.
  21. K.K. Kim et al., "Learning-Based Approach, for License Plate Recognition," Proc. IEEE Signal Process. Soc. Workshop, Neural Netw. Signal Process., Dec. 11-13, 2000, pp. 614-623.
  22. P. Comelli et al., "Optical Recognition of Motor Vehicle License Plates," IEEE Trans. Veh. Technol., vol. 44, no. 4, Nov. 1995, pp. 790-799. https://doi.org/10.1109/25.467963
  23. Y.P. Huang, S.Y. Lai, and W.P. Chuang, "A Template-Based Model for License Plate Recognition," IEEE Int. Conf. Netw., Sensing Contr., Taipei, Taiwan, Mar. 21-23, 2004, pp. 737-742.
  24. R. Gonzalez and R. Woods, Digital Image Processing, Englewood Cliffs, NJ, USA: Prentice Hall, 2002.
  25. N. Otsu, "A Threshold Selection Method from Gray-Level Histograms," IEEE Trans. Syst., Man, Cybern., vol. 9, no. 1, Jan. 1979, pp. 62-66. https://doi.org/10.1109/TSMC.1979.4310076
  26. M.V. Nagendraprasad, P.S.P. Wang, and A. Gupta, "Algorithms for Thinning and Rethickening Binary Digital Patterns," Digital Signal Process., vol. 3, no. 2, Apr. 1993, pp. 97-102. https://doi.org/10.1006/dspr.1993.1014
  27. M. Cheriet et al., Character Recognition Systems: a Guide for Students and Practitioner, Hoboken, NJ, USA: John Wiley & Sons, 2007.
  28. J.A. Anderson, "Introduction to Neural Networks," Handbook of Brain Theory and Neural Networks, Cambridge, MA, USA: MIT Press, 1995.
  29. J. Heaton, Introduction to Neural Networks for Java, 2nd Edition, Heaton Research, 2008, pp. 158-159.

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