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A Novel Approach for Vehicle License Plate Localization and Recognition

by Muhammad H Dashtban, Zahra Dashtban, Hassan Bevrani
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 26 - Number 11
Year of Publication: 2011
Authors: Muhammad H Dashtban, Zahra Dashtban, Hassan Bevrani
10.5120/3167-4382

Muhammad H Dashtban, Zahra Dashtban, Hassan Bevrani . A Novel Approach for Vehicle License Plate Localization and Recognition. International Journal of Computer Applications. 26, 11 ( July 2011), 22-30. DOI=10.5120/3167-4382

@article{ 10.5120/3167-4382,
author = { Muhammad H Dashtban, Zahra Dashtban, Hassan Bevrani },
title = { A Novel Approach for Vehicle License Plate Localization and Recognition },
journal = { International Journal of Computer Applications },
issue_date = { July 2011 },
volume = { 26 },
number = { 11 },
month = { July },
year = { 2011 },
issn = { 0975-8887 },
pages = { 22-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume26/number11/3167-4382/ },
doi = { 10.5120/3167-4382 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:12:32.653328+05:30
%A Muhammad H Dashtban
%A Zahra Dashtban
%A Hassan Bevrani
%T A Novel Approach for Vehicle License Plate Localization and Recognition
%J International Journal of Computer Applications
%@ 0975-8887
%V 26
%N 11
%P 22-30
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, a general approach for international vehicle license plate localization and recognition is proposed. A hybrid solution is presented with combining basic machine vision techniques and neural networks. The proposed model consists of three main parts, including localization, segmentation and recognition. In the license plate localization, after some essential preprocessing and finding edges, the 8-connectivity of image background eliminates which helps more appropriately separating of main image objects from the cluttered backgrounds. Then, it is tried to find connected objects with 8-connectivity of the differentiated binary image. The binarization of license plate is based on local binarizing. The proposed recognizing system utilizes the Hough transform, basic morphological operators and Skeletonizing to provide an appropriate input for artificial neural networks. Segment by segment, the input streams into an intelligent error control unit (IECU) which itself is an already trained multi-layer perceptron (MLP) neural network. IECU investigates empty or non-character–inside segments. In case of no error, each segment streams into two already trained MLPs. Each of them singly recognizes either the alphabets or numbers. We show that this approach achieves accuracy over 91% on localizing vehicle license plate. The image database includes images of various vehicles with different background and slop under varying illumination conditions. The character recognition system correctly recognizes alphabets with probability over 97% and over 94% in case of numbers.

References
  1. Satadal Saha, Subhadip Basu, Mita Nasipuri, Dipak Kr. Basu, “Localization of License Plates from Surveillance Camera Images: A Color Feature Based ANN Approach”, International Journal of Computer Applications, 2010, Volume 1 – No. 23
  2. M. H. Brugge, J. H. Stevens, J. A. G. Nijhuis, L. Spaanenburg, “License Plate Recognition Using DTCNNs”, in proc. IEEE lnt. Workshop on Cellular Neural Networks and their Applications, 1998, pp. 212-217.
  3. Mario I. Chacon M., Alejandro Zimmerman S., “License Plate Location Based on a Dynamic PCNN Scheme”, in proc. Int. Joint Conf. on Neural Networks, vol. 2, 2003, pp. 1195 – 1200.
  4. K. K. Kim, K. I. Kim, J. B. Kim, H. J. Kim, “Learning-Based Approach, for License Plate Recognition”, in proc. IEEE Signal Processing Society Workshop, Neural Networks for Signal Processing, Vol. 2, 2000, pp. 614 - 623.
  5. K. I. Kim, K. Jung, J. H. Kim, Color texture-based object detection: an application to license plate localization, in: S.-W. Lee, A. Verri (Eds.), Lecture Notes on Computer Science, vol. 2388, Springer, New York, 2002, pp. 293–309.
  6. S. Draghici, A neural network based artificial vision system for license plate recognition, International Journal Neural Systems 8 (1997) 113–126.
  7. R. Parisi, E. D. Di Claudio, G. Lucarelli, G. Orlandi, Car plate recognition y neural networks and image processing, IEEE International Symposium on Circuits and Systems 3 (1998) 195–198.
  8. Gang Li, Ruili Zeng, Ling Lin, Research on vehicle license plate location based on neural networks, in: First International Conference on Innovative Computing, Information and Control, 2006.
  9. X. Shi, W. Zhao, Y. Shen, Automatic license plate recognition system based on color image processing, in: O. Gervasi et al. (Eds.), Lecture Notes on Computer Science, vol. 3483, Springer, New York, 2005, pp. 1159–1168.
  10. E. R. Lee, P.K. Kim, H. J. Kim, Automatic recognition of a car license plate using color image processing, in: IEEE International Conference on Image Processing,Austin, Texas, 1994, pp. 301–305.
  11. M. Zahedi ,S. M. Salehi. License Plate Recognition System Based on SIFT Features, Procedia Computer Science,3,2011
  12. M. A. Ko, Y. M. Kim, License plate surveillance system using weighted template matching, in: Proceedings of Applied Imagery Pattern Recognition Workshop, 2003, pp. 269–274.
  13. Y. P. Huang, S.Y. Lai, W.P. Chuang, A template-based model for license plate recognition, in: Proceedings of the 2004 IEEE International Conference on Networking, Sensing & Control, vol. 2, 2004, pp. 737–742.
  14. V. Shapiro, G. Gluhchev, Multinational license plate recognition system: segmentation and classification, in: ICPR, vol. 4, 2004, pp. 352–355.
  15. H. A. Hegt, R. J. De la Haye, N.A. Khan, A high performance license plate recognition system, in: Proceedings of IEEE International Conference on System, Man and Cybernetics, vol. 5, 1998, pp. 4357–4362.
  16. M. M. Shidore, S. P. Narote, Number Plate Recognition for Indian Vehicles, International Journal of Computer Science and Network Security, VOL.11 No.2, Feb. 2011
  17. J. Barroso, A. Rafel, E. L. Dagless, J. BulasCruz, Number plate reading using computer vision, IEEE Int. Symp. Ind. Electron. ISIE'97 3 (1997) 761–766.
  18. N. Xin, S. Lansun, Research on license plate recognition technology. Measurement & Control Technology, 1999, pp. 14–17 (in Chinese).
  19. K. M. Hung,C.T. Hsieh, Real-Time Mobile Vehicle License Plate Detection and Recognition, Tamkang Journal of Science and Engineering, Vol. 13, No. 4, pp. 433_442 (2010)
  20. D. H. Ballard, C. M. Brown, Computer Vision, Pages 151-152, Prentice Hall, New Jersey, 1982.
  21. H. E. Kocer, K. K. Cevik, Artificial neural networks based vehicle license plate recognition, Procedia Computer Science, Volume 3, 2011, Pages 1033-1037
  22. F. Kahraman, B. Kurt,M. Gökmen, License Plate Character Segmentation Based on the Gabor Transform and Vector Quantization,ISCIS 2003,LNCS 2869,pp. 381-388,2003.
  23. Y. Cui, Q. Huang, “Extracting characters of license plates from video sequences”, Machine Vision and Applications, vol. 10, pp. 308-320, 1998.
  24. Y. R. Wang, W. H. Lin, S. J. Horng, A sliding window technique for efficient license localization based on discrete wavelet transform, Expert Systems with Applications, Volume 38, Issue 4, April 2011, Pages 3142-3146
  25. H. Ibrahim, N. S. P. Kong,” Image Sharpening Using Sub-Regions Histogram Equalization”, IEEE T CONSUM ELECTR, 55, 891–895, 2009.
  26. N. Otsu, “A threshold selection method from gray-level histogram, IEEE Transactions on Systems”, Man, and Cybernetics 9 (1979) 62–66.
  27. Lim, Jae S., Two-Dimensional Signal and Image Processing, Englewood Cliffs, NJ, Prentice Hall, 1990, pp. 469-476.
  28. C. A. Rahman, A. Radmanesh, A Real Time Vehicle’s License Plate Recognition System,Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance, 2003.
  29. C. C. Chen, J. W. Hsieh, License Plate Recognition from Low-Quality Videos, VA2007 IAPR Conference on Machine Vision Applications, May 16-18, 2007.
  30. A. Broumandnia, M. Fathy, Application of pattern recognition for Farsi license plate recognition, 2005
  31. J. A. G. Nijhuis, M. H. ter Brugge, K.A. Helmholt, J.P.W. Pluim, L. Spaanenburg,R.S. Venema, M.A. Westenberg, “Car License Plate Recognition with Neural Networks and Fuzzy Logic”, in proc.. IEEE International Conference on Neural Networks, vol.5, 1995, pp. 2232-2236.
  32. A. Akoum, B. Daya , P. Chauvet, ”two neural networks for license number plates recognition”, Journal of Theoretical and Applied Information Technology, 2005 - 2009 JATIT.
  33. Levenberg, K., "A Method for the Solution of Certain Problems in Least Squares," Quart. Appl. Math. Vol. 2, pp 164-168, 1944.
  34. Marquardt, D., "An Algorithm for Least-Squares Estimation of Nonlinear Parameters," SIAM J. Appl. Math. Vol. 11, pp 431-441, 1963.
Index Terms

Computer Science
Information Sciences

Keywords

License plate LPR character recognition Hough transform neural network recognition Multi layer perceptron plate recognition character segmentation plate localization diagonal fill edge Sobel OCR low pass filter Gaussian TSR

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