Automatic License Plate Recognition in Kurdistan Region of Iraq (KRI)

Authors

  • Abbas Mohamed Ali Software Engineering Department, College of Engineering, Salahaddin University, Erbil, Kurdistan Region, Iraq. Author
  • Shareef Maulod Shareef Software Engineering Department, College of Engineering, Salahaddin University, Erbil, Kurdistan Region, Iraq. Author
  • Tarik Ahmed Rashid Software Engineering Department, College of Engineering, Salahaddin University, Erbil, Kurdistan Region, Iraq. Author

DOI:

https://doi.org/10.17656/jzs.10417

Keywords:

E-Government, Gabor Feature Vectors, License Plate Recognition, SVM

Abstract

The development of countries increases the number of vehicles on the roads now than there used to be. Consequently, controlling and managing the congestion of traffic is virtually difficult without the use of computer technology. This paper aims to identify automatic license plate recognition (ALPR) of vehicles in Kurdistan Region of Iraq (KRI). It uses computer vision techniques where a cluster of Gabor feature vectors using K-means is used, furthermore, the resulted cluster feature is optimized with Wrapper Sub Eval technique to reduce the dimensionality of features vectors, then, the optimized features are fed into classification techniques such as Support Vector Machines (SVMs), K-Nearest neighbors (K-NN) and Radial Basis Function (RBF) Neural Network in order to examine the recognition rate of the license plate of the vehicle automatically. The experimental work shows that the proposed technique produced promising classification results in recognizing license plate of vehicles. The best optimal accuracy result under various illumination conditions was 96.72.

References

Shareef, S. Hamid J. and Muhammad D. 2012. “E-Government Stage Model: Based On Citizen-Centric approach In

the Regional Government In Developing Countries” International Journal of Electronic Commerce Studies Vol.3, No.1,

pp.145-164.

Satadal B, S Mita N and Dipak K. 2009. “An Offline Technique for Localization of License Plates for Indian

Commercial Vehicles” Proceedings of National Conference on Computing and Communication Systems (COCOSYS-

held in University Institute of Technology, University of Burdwan on January 02-04, pp. 206-211.

Kahraman F B, Gokmen M. 2003. License Plate Character Segmentation Based on the Gabor Transform and Vector DOI: https://doi.org/10.1007/978-3-540-39737-3_48

Quantization. In: International Symposium on Computer and Information Sciences.

Arthet, A Limberger, F and Bischof, H. 2007. “Real-Time License Plate Recognition on an Embeded DSP DOI: https://doi.org/10.1109/CVPR.2007.383412

Platform”, International Conference on Computer Vision and Pattern Recognition.

Bailey D G. Irecki D Lim B K and Yang L 2009. “Test Bed for Number Plate Recognition Applications”,

Proceeding of the IEEE International Workshop on Electronic Design, Test and Application.

Nguyen C Ardabilian, M and Chen L. 2010. “Unifying Approach for Fast License Plate Localization and Super- DOI: https://doi.org/10.1109/ICPR.2010.100

Resolution”.

Kolour S H, Shahbahrami A. 2011.”An Evaluation of License Plate Recognition Algorithms” International Journal

of Digital Information and Wireless Communications (IJDIWC) 1(1): 247-253. The Society of Digital Information and

Wireless Communications, 2011(ISSN 2225-658X).

Psyllos P, Anagnostopoulos N A and E Kayafas 2010. “Vehicle logo recognition using a SIFT-based enhanced DOI: https://doi.org/10.1109/TITS.2010.2042714

matching scheme,” IEEE Trans, On Intell. Transp. Sys., vol. 11, pp. 322–328.

Nig M. Jun L and Hong Z. 2011. “Robust Description Method of SIFT for Features of License Plate Characters”,

information technology journal 10(11). DOI: https://doi.org/10.1108/09593849710166138

Aghaie, M Shokri, F and Tabari, Z Y. 2013. “Automatic Iranian Vehicle License Plate Recognition System Based DOI: https://doi.org/10.18495/comengapp.v2i2.25

on Support Vector Machine (SVM) Algorithms” Computer Engineering and Applications Vol. 2(1).

Maro M Chacon, and Alejandro S Z. 2003. "License Plate Location based on a Dynamic PCNN Scheme", IEEE

International Symposium on Computational Intelligence in Robotics and Automation.

Muhammad H Dashtban, Zahra Dashtban , Shahid Rajee, Hassan Bevrani. 2011. A Novel Approach for Vehicle

License Plate Localization and Recognition International Journal of Computer Applications (0975 – 8887) Volume 26–

No.11, July 2011 22.

Gazcn F N, Chesevar I. C. and Castro M C. 2012. “Automatic vehicle identification for Argentinean License

plates using intelligent template matching” Pattern Recognition Letters 33, pp 1066–1074.

Jesper J H, 2007. 3D surface tracking and approximation using Gabor filters, South Denmark University.

Haghighat M. Zonouz S. 2013. Abdel-Mottaleb, M. "Identification Using Encrypted Biometrics". Computer

Analysis of Images and Patterns, Lecture Notes in Computer Science 8048.p. 40.doi:10.1007/978-3-642-40246-3_55. DOI: https://doi.org/10.1007/978-3-642-40246-3_55

ISBN 978-3-642-40245-6.

Andries P Engelbrecht. 2007. “Computational Intelligence: An Introduction”, 2nd Edition, John Wiley & Sons, DOI: https://doi.org/10.1002/9780470512517

Ltd, Chichester, England.

Al-Amin B and Chang H. L 2007. On Face Recognition using Gabor Filters World Academy of Science,

Engineering and Technology, pp 51-56, 28.

Wathsala N W, 2009. BSc. Project on “Facial Emotion Recognition with a Neural Network Approach”, School of

Computing, University of Colombo, Sri Lanka, August.

Michel F V, Patras I and Maja P. 2005. “Facial Action Unit Detection Using Probabilistic Actively Learned

Support Vector Machines on Tracked Facial Point Data”, IEEE Conference on Computer Vision and Pattern

Recognition, California, USA.

Mark J L Orr, 1996. “Introduction to Radial Basis Function Networks”, Centre for Cognitive Science, University

of Edinburgh, Edinburgh, Scotland.

Chih-Wei Hsu C, and Chih-Jen L. 2010. “A Practical Guide to Support Vector Classification”, Department of

Computer Science, National Taiwan University, Taipei, Taiwan.

Chih-Chung C., Chih-Jen L. 2013. “LIBSVM - A Library for Support Vector Machines”, Department of

Computer Science, National Taiwan University,Taipei, Taiwan, Availavle at: http://www.csie.ntu.edu.tw/~cjlin/libsvm/

(Accessed on: 20 August 2014).

Rashid T, and Abdul-Hamid, S. 2014, Support Vector Machines for Predicting Electrical Faults, in the process to DOI: https://doi.org/10.30684/etj.32.8A4

be published in International Journal, University Technology, Baghdad, Iraq.

Ali, M. A. Md. Jan Nordin and Abdullah A 2014. A Spatial Visual Word of Discrete Image Scene for Indoor

Localization, Research Journal of Applied Sciences, Engineering and Technology 7(14): 2806- 2812, 2014 ISSN: 2040- DOI: https://doi.org/10.19026/rjaset.7.603

; e-ISSN: 2040-7467 © Maxwell Scientific Organization.

Published

2015-06-25

How to Cite

Automatic License Plate Recognition in Kurdistan Region of Iraq (KRI). (2015). Journal of Zankoy Sulaimani - Part A, 17(3), 235-244. https://doi.org/10.17656/jzs.10417