CFP last date
15 October 2024
Reseach Article

On the Automatic Recognition of Saudi License Plate

by Khaled M. Almustafa
International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 5 - Number 1
Year of Publication: 2013
Authors: Khaled M. Almustafa
10.5120/ijais12-450839

Khaled M. Almustafa . On the Automatic Recognition of Saudi License Plate. International Journal of Applied Information Systems. 5, 1 ( January 2013), 34-44. DOI=10.5120/ijais12-450839

@article{ 10.5120/ijais12-450839,
author = { Khaled M. Almustafa },
title = { On the Automatic Recognition of Saudi License Plate },
journal = { International Journal of Applied Information Systems },
issue_date = { January 2013 },
volume = { 5 },
number = { 1 },
month = { January },
year = { 2013 },
issn = { 2249-0868 },
pages = { 34-44 },
numpages = {9},
url = { https://www.ijais.org/archives/volume5/number1/409-0839/ },
doi = { 10.5120/ijais12-450839 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T16:00:51.578997+05:30
%A Khaled M. Almustafa
%T On the Automatic Recognition of Saudi License Plate
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 5
%N 1
%P 34-44
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper three different algorithms for Automatic License Plate Recognition (ALPR) of the Saudi License Plates are described. All algorithms rely on processing information from lines strategically drawn vertically and horizontally through a character. The first algorithm calculates the number of peaks for each line. A peak is a place in the line where the pixels change from black to white. The second algorithm calculates the pixels density for a specific crossing line in a character. Pixel density is defined as the number of pixels having a specific intensity level to the total number of pixels in a line. The third algorithm calculates the position of the peaks introduced in the first algorithm rather than only their numbers. An algorithm was developed for each method to differentiate between all characters of the license plate. Uniformly distributed pseudo-random noise was added to simulate the performance of these algorithms in the presence of noisy images, also performance of the suggested algorithms were tested due to image rotation. A comparison between these algorithms also presented. These algorithms were proven to work even in some cases in which the characters were extremely degraded by noise.

References
  1. Hasan Obeid and Rached Zantout, "Line Processing: An Approach to ALPR Character Recognition", ACS/IEEE International Conference on Computer Systems and Applications, Amman, Jordan, May 13-16, 2007.
  2. S. Hamidreza Kasaei, S. Mohammadreza Kasaei, S. Alireza Kasaei, New Morphology-Based Method for Robust Iranian Car Plate Detection and Recognition, International Journal of Computer Theory and Engineering, Vol. 2, No. 2 April, 2010, 1793-8201.
  3. Wenjing Jia, , Huaifeng Zhang, Xiangjian He, Region-based license plate detection, Journal of Network and Computer Applications, Volume 30, Issue 4, November 2007, Pages 1324-1333.
  4. Wisam Al Faqheri and Syamsiah Mashohor, A Real-Time Malaysian Automatic License Plate Recognition (M-ALPR) using Hybrid Fuzzy, IJCSNS International Journal of Computer Science and Network Security, VOL. 9 No. 2, February 2009.
  5. Bacel Agha, Majed Yehya, Mazen Jerman, Tarek Hattab, and Khalil Sidawi, "Arabic Optical Character Recognition System," Beirut Arab University, 2004-2005, pp. 5-19.
  6. http://cslu. cse. ogi. edu/HLTsurvey/ch2node6. html, Survey of the State of the Art in Human Language Technology (1996)
  7. Remus Brad, "License Plate Recognition System," Proceedings of the 3rd International Conference on Information, Communications and Signal Processing, Singapore, October 2001
  8. Ponce, P. , Wang, S. S. & Wang, D. L. (2001). License Plate Recognition. Report, Department of Electrical and Computer Engineering, Carnegie Mellon University. Retrieved from http://www. ece. cmu. edu/~ee551/Final_Reports/Gr18. 551. S00. pdf.
  9. Ye Wang, Honggang Zhang, Xu Fang, and Jun Guo, "Low-Resolution Chinese Character Recognition of Vehicle License Plate Based on ALBP and Gabor Filters", 2009 Seventh International Conference on Advances in Pattern Recognition, February 4-9, Kolkata, India.
  10. David Chanson and Timothy Roberts, "License Plate Recognition System," Department of Electrical and Electronic Engineering, Manukau Institute of Technology, Auckland.
  11. LV Fang , Zhang song-yu and HU lin-jing, "Image Extraction and Segment Arithmetic of License Plate Recognition", 2nd International Conference on Power Electronics and Intelligent Transportation System, Dec. 19, 2009, Shenzhen, China.
  12. Serkan Ozbay and Ergun Ercelebi, "Automatic Vehicle Identification by Plate Recognition," Transactions on Engineering, Computing and Technology, version 9, November 2005, ISSN 1305-5313.
  13. Muhammad Sarfraz, Mohammed Jameel Ahmed, Syed A. Ghazi, "Saudi Arabian License Plate Recognition System," International Conference on Geometric Modeling and Graphics, , pp. 36, 2003 International Conference on Geometric Modeling and Graphics (GMAG'03), London, England, July 16-July 18.
  14. Broumandnia, A. & Fathy, M. (2005, January). Application of Pattern Recognition for Farsi License Plate Recognition," International Journal on Graphics, Vision and Image Processing. Volume 5. Issue 2. (pp. 25-31).
  15. Halina Kwasnicka and Bartosz Wawrzyniak, "License Plate Localization and Recognition in Camera Pictures", Faculty Division of Computer Science, Wroclaw University of Technology, Artificial Intelligence Methods, November 13-15, 2002, Gliwice, Poland.
  16. Van Heerden, R. P. &Botha, E. C. (2010, November). Optimization of Vehicle License Plate Segmentation and Symbol Recognition. The 21st Annual International Symposium of the Pattern Recognition Association of South Africa. Stellenbosch, South Africa.
  17. V. Turchenko, V. Kochan, V. Koval, A. Sachenko and G. Markowsky, "Smart Vehicle Screening System Using Artificial Intelligence Methods," Proceedings of 2003 Spring IEEE Conference on Technologies for Homeland Security, May 7-8, 2003, Cambridge, MA, pp. 182-185.
  18. Rafael C. Gonzalez and Richard E. Woods, "Digital Image Processing," Second edition, Prentice Hall, pp. 523-532, 2002, ISBN: 0130946508.
  19. Khaled Almustafa, Rached N. Zantout, Hasan R. Obeid and Fadi Sibai "Recognizing Characters in Saudi License Plates Using Character Boundaries," International Conference on Innovations in Information Technology, 2011 pp. 415 – 420, Abudabi, UAE
  20. Khaled Almustafa, Rached N. Zantout, Hasan R. Obeid, "Pixel Density: Recognizing Characters in Saudi License Plates," 10th International Conference on Intelligent Systems Design and Applications, 2010, ISDA 2010 Egypt.
  21. Khaled Almustafa, Rached N. Zantout, Hasan R. Obeid, "Peak Position, Recognizing Characters in Saudi License Plates," 2011 IEEE GCC Conference and Exhibition for Sustainable Ubiquitous Technology, Dubai, UAE, February
Index Terms

Computer Science
Information Sciences

Keywords

ALPR Line Processing Pixel Density Number of Peaks Position of Peaks Segmentation