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The Kingdom of Saudi Arabia Vehicle License Plate Recognition using Learning Vector Quantization Artificial Neural Network

by Yusuf Perwej, Nikhat Akhtar, Firoj Parwej
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 98 - Number 11
Year of Publication: 2014
Authors: Yusuf Perwej, Nikhat Akhtar, Firoj Parwej
10.5120/17230-7556

Yusuf Perwej, Nikhat Akhtar, Firoj Parwej . The Kingdom of Saudi Arabia Vehicle License Plate Recognition using Learning Vector Quantization Artificial Neural Network. International Journal of Computer Applications. 98, 11 ( July 2014), 32-38. DOI=10.5120/17230-7556

@article{ 10.5120/17230-7556,
author = { Yusuf Perwej, Nikhat Akhtar, Firoj Parwej },
title = { The Kingdom of Saudi Arabia Vehicle License Plate Recognition using Learning Vector Quantization Artificial Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { July 2014 },
volume = { 98 },
number = { 11 },
month = { July },
year = { 2014 },
issn = { 0975-8887 },
pages = { 32-38 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume98/number11/17230-7556/ },
doi = { 10.5120/17230-7556 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:25:58.078783+05:30
%A Yusuf Perwej
%A Nikhat Akhtar
%A Firoj Parwej
%T The Kingdom of Saudi Arabia Vehicle License Plate Recognition using Learning Vector Quantization Artificial Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 98
%N 11
%P 32-38
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the today scenario technological intelligence is a higher demand after commodity even in traffic-based systems. These intelligent systems do not only help in traffic monitoring but also in commuter safety, law enforcement and commercial applications. The proposed Saudi Arabia Vehicle License plate recognition system splits into three major parts, firstly extraction of a license plate region secondly segmentation of the plate characters and lastly recognition of each character. This act is quite challenging due to the multiformity of plate formats and the nonuniform outdoor illumination conditions during image collection. In this paper recognition of the license plates is achieved by the implementation of the Learning Vector Quantization artificial neural network. Their results are based upon their completeness in the Saudi Arabia Vehicle License plate character recognition and theirs have obtained encouraging results from proposed technique.

References
  1. P. Comelli, P. Ferragina,M. N. Granieri, and F. Stabile, "Optical recognition of motor vehicle license plates," IEEE Trans. Veh. Technol. , vol. 44, no. 4, pp. 790–799, Nov. 1995.
  2. S. -L. Chang, L. -S. Chen, Y. -C. Chung, and S. -W. Chen, "Automatic license plate recognition," IEEE Trans. Intell. Transp. Syst. , vol. 5, no. 1, pp. 42–53, Mar. 2004.
  3. V. Abolghasemi, A. Ahmadyfard, An edge-based color-aided method for license plate detection. Image and Vision Computing 27, pp. 1134-1142, 2009.
  4. Dubey, P. , "Heuristic Approach for License Plate Detection", IEEE Conference on Advanced Video and Signal Based Surveillance, 15-16 Sept. 2005, pp. 366-370.
  5. W. Pan, R. An, Morphology and auto -correlation based method of fast locating vehicle license plate. In: Proc. Of the 2nd Internat. Conf. Advanced Computer Control, vol. 3, pp. 116-119, 2010.
  6. B. D Ripley, Pattern Recognition and Neural Networks, Cambridge University Press ISBN 0 521 46986 7, 1996.
  7. A. Sato, K. Yamada, Generalized learning vector quantization, Advances in NIPS, MIT Press, pp. 423-429, Vol. 7, 1995.
  8. C. Anagnostopoulos, I. Anagnostopoulos, E. Kayafas, and V. Loumos. "A License Plate Recognition System for Intelligent Transportation System Applications", IEEE Trans. Intell. Transp. Syst. , 7(3), pp. 377– 392, Sep. 2006.
  9. M. Wu, L. Wei, H. Shih and C. C. Ho. "License Plate Detection Based on 2-Level 2D Haar Wavelet Transform and Edge Density Verification", IEEE International Symposium on Industrial Electronics (ISlE), pp: 1699-1705, 2009.
  10. T. -H. Wang, F. -C. Ni, K. -T. Li, and Y. -P. Chen, "Robust license plate recognition based on dynamic projection warping," in Proc. IEEE Int. Conf. Netw. , Sens. Control, 2004, pp. 784–788.
  11. Dr. Firoj Parwej , " A Perceptive Method for Arabic Word Segmentation using Bounding Boxes by Morphological Dilation", for published in the International Journal of Computer Applications (IJCA) USA , Volume 71, No. 1, Pages 1 – 7, ISSN 0975 – 8887, June 2013.
  12. Youngwoo Yoon, Kyu-Dae Ban, Hosub Yoon, and Jaehong Kim, "Blob Extraction based Character Segmentation Method for Automatic License Plate Recognition System", Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Anchorage, Alaska, USA, IEEE ISBN 978-1-4577-0652-3, October 9-12, 2011.
  13. Hossan, M. A. "A Novel Approach for MFCC Feature Extraction", 4th International Conference on Signal Processing and Communication Systems (ICSPCS), pp. 1-5, Dec 2010
  14. P. D. Gader, M. Khabou, Automatic feature generation for handwritten digit recognition. IEEE Trans. Pat- tern Analysis and Machine Intelligence 18(12) 1256{1262 (1996)
  15. D. Trier , A. K. Jain and T. Taxt, "Feature extraction methods for character recognition-A survey", Pattern Recognition, vol. 29, no. 4, (1996), pp. 641-662.
  16. G. L. Zeng "Nonuniform noise propagation by using the ramp filter in fan-beam computed tomography", IEEE Trans. Med. Imag. , vol. 23, no. 6, pp. 690 -695 2004.
  17. F. Noo, M. Defrise, R. Clackdoyle, and H. Kudo, "Image reconstruction from fan-beam projections on less than a short scan", Phys. Med. Biol. , vol. 47, pp. 2525 -2546 2002.
  18. J. W. Beattie, "Tomographic Reconstruction from Fan Beam Geometry Using Radon's Integration Method", IEEE Transactions on Nuclear Science, vol. 22, no. 1, (1975) February, pp. 359-363.
  19. Yusuf Perwej , Firoj Parwej, "A Neuroplasticity (Brain Plasticity) Approach to Use in Artificial Neural Network" for published in the International Journal of Scientific & Engineering Research (IJSER), France , Vol. 3, Issue 6, pp 1- 9, ISSN 2229 – 5518, June 2012.
  20. A. Sato, K. Yamada, Generalized learning vector quantization, Advances in NIPS, MIT Press, pp. 423-429, Vol. 7, 1995.
  21. T. Kohonen, The Self-organizing Map, Proceedings of the IEEE, pp. 1464-1480, 1990.
  22. K. L. Wu and M. S Yang, Alternative learning vector quantization, Pattern Recognition Journal, pp. 351-362, Vol. 39, 2006.
  23. M. Biehl, A. Ghosh, B. Hammer, Dynamics and Generalization Ability of LVQ Algorithms, The Journal of Machine Learning Research, pp. 323-360, Vol. 8, 2007.
  24. C. Zhan, X. Lu, M. Hou, and X. Zhou, "A LVQ-based neural network anti-spam email approach", ACM SIGOPS Operating Systems Review Volume 39 , Issue, USA, 2005 pp. 34-39.
Index Terms

Computer Science
Information Sciences

Keywords

Arabic Character Segmentation Learning Vector Quantization Neural Network Fan-Beam Feature Extraction Vehicle License Plate Extraction.

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