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Intelligent License Plate Recognition

  • Yaecob Girmay Gezahegn
  • Misgina Tsighe Hagos
  • Dereje H. Mariam W. Gebreal
  • Zeferu Teklay Gebreslassie
  • G. agziabher Ngusse G. Tekle
  • Yakob Kiros T. Haimanot
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 244)

Abstract

Road traffic accident is the leading cause of deaths and injuries in the world according to the World Health Organization (WHO). Every year many deaths and injuries are reported and most of them are in developing countries; the problem has great impact in Africa. Intelligent License Plate Recognition and reporting (ILPR) plays an important role in minimizing traffic accidents by implementing traffic monitoring and management systems. Since the number of vehicles are increasing, breaking traffic rules, entering restricted areas are becoming a trend. So, to control these actions, a system which can recognize vehicles by their License Plate (LP) is crucial. In this paper, we have developed ILPR system, which aims at reducing traffic accidents by processing an input image of a vehicle and reporting on its legality status. The ILPR starts with preprocessing and then extracts the LP using edge detection and vertical projection algorithms. To identify the License Plate Number (LPN), characters found on the LP are extracted and recognized by Artificial Neural Networks (ANN), which we trained with sample characters. If the recognized LPN is found to be a suspect after cross checking it with a pre-stored database, it will be sent to a person in charge via Short Message Service (SMS). In the recognition part, different papers use template matching, but is sensitive to noise. In order to mitigate the noise problem, our system uses ANN. We have also added SMS module. The system is implemented using MATLAB and Java.

Keywords

Filtering Edge detection Segmentation Recognition Artificial neural networks Vertical projection Template matching 

References

  1. 1.
    World Health Organization (WHO): Global Status Report on Road Safety (2015)Google Scholar
  2. 2.
    Ethiopian Roads Authority: How Safe are Ethiopian Roads, April 2013Google Scholar
  3. 3.
    Tulu, G.S., et al.: Characteristics of police-reported road traffic crashes in Ethiopia over a six year period. In: Proceedings of the 2013 Australasian Road Safety Research, Policing & Education Conference, August 2013Google Scholar
  4. 4.
    Persson, A.: Road traffic accidents in Ethiopia: magnitude, causes and possible interventions. Adv. Transp. Stud. 15, 5–16 (2008)Google Scholar
  5. 5.
    Mekasha, F.: Road traffic accident: causes and control mechanisms. Master’s thesis, Addis Ababa University, Addis Ababa City, June 2015Google Scholar
  6. 6.
    Kopits, E.: Traffic Fatalities and Economic Growth. Policy Research Working Paper Number 3035, World Bank, Washington, DC (2003)CrossRefGoogle Scholar
  7. 7.
    Bliss, T., et al.: Implementing the recommendations of the world report on road traffic injury prevention. World Bank Global Road Safety (2009)Google Scholar
  8. 8.
    Cowell, J.R.: Syntactic pattern recognizer for vehicle identification numbers. Image Vis. Comput. 13(1), 13–19 (1995)CrossRefGoogle Scholar
  9. 9.
    Lotufo, R.A., et al.: Automatic number plate recognition. Inst. Elect. Eng. Colloquium on Image Analysis for Transport Applications, pp. 61–66 (1990)Google Scholar
  10. 10.
    Bartolome, L.S., et al.: Vehicle parking inventory system utilizing image recognition through artificial neural networks, pp. 1–5, IEEE, May 2012Google Scholar
  11. 11.
    Draghici, S.: A neural network based artificial vision system for license plate recognition. Int. J. Neural Systems 8, 113–126 (1997)CrossRefGoogle Scholar
  12. 12.
    Lotufo, R.A., et al.: Automatic license plate recognition a state-of-the-art review. Ieee Trans. Circ. Syst. Video Technol. 23, 311–325 (2013)CrossRefGoogle Scholar
  13. 13.
    Zhang, H., et al.: Learning-based license plate detection using global and local features. Int. Conf. Pattern Recognit. 2, 1102–1105 (2006)Google Scholar
  14. 14.
    Al-Ghaili, A.M., et al.: A new character segmentation is successful when the character Vertical edge detection algorithm and its application. In: Proceedings of International Conference on Computer Engineering Systems, pp. 204–209 (2008)Google Scholar
  15. 15.
    Zheng, D., et al.: An efficient method of license plate location. Pattern Recogn. Lett. 26(15), 2431–2438 (2005)CrossRefGoogle Scholar
  16. 16.
    Lee, H.J., et al.: Extraction and recognition of license plates of motorcycles and vehicles on highways. In: Proceedings of ICPR, pp. 356–359 (2004)Google Scholar
  17. 17.
    Hsieh, C.T., et al.: Multiple license plate detection for complex background. Int. Conf. AINA 2, 389–392 (2005)Google Scholar
  18. 18.
    Lee, E.R., et al.: Automatic recognition of a car license plate using color image processing. In: Proceedings of International Conference on Image Processing (1994)Google Scholar
  19. 19.
    Kim, S.K., et al.: A Recognition of vehicle license plate using a genetic algorithm based segmentation. In: Proceedings of 3rd IEEE International Conference, pp. 661–664 (1996)Google Scholar
  20. 20.
    Tavsanoglu, V., Saatci, E.: Feature extraction for character recognition using Gabor-type filters implemented by cellular neural networks. In: Proceedings of the 6th IEEE International Workshop, pp. 63–68, Piscataway, NJ, USA. IEEE (2000)Google Scholar
  21. 21.
    Ahmed, M.J., et al.: License plate recognition system. In: ICECS, Proceedings of the 2003 10th IEEE International Conference, vol. 2, pp. 898–901, December 2003Google Scholar
  22. 22.
    Kim, K.K., et al.: Learning-based approach for license plate recognition. In: Proceedings of the IEEE Signal Processing Society Workshop, vol 2, pp. 614–623 (2000)Google Scholar
  23. 23.
    Comelli, P., Ferragina, P., Granieri, M.N., Stabile, F., et al.: Optical recognition of motor vehicle license plates. IEEE Trans. Veh. Tech. 44, 790–799 (1995)CrossRefGoogle Scholar
  24. 24.
    Zhu, S.: An end-to-end license plate localization and recognition system. Master thesis. Rochester Institute of Technology, New York, March 2015Google Scholar
  25. 25.
    Ahmed, H.Z.: Design and implementation of car plate recognition system for ethiopian car plates. Master’s thesis, Addis Ababa, November 2011Google Scholar
  26. 26.
    Nigussie, S., Assabie, Y.: Automatic recognition of ethiopian license plates. In: IEEE AFRICON, Addis Ababa, Ethiopia (2015)Google Scholar
  27. 27.
    Pan, R., et al.: An efficient method for skew correction of license plate. In: Second International Workshop, vol. 2, pp. 90–93, March 2010Google Scholar
  28. 28.
    Park, S.H., et al.: Locating car license plates using neural networks. Electron. Lett. 35, 1475–1477 (1999)CrossRefGoogle Scholar
  29. 29.
    Arth, C., et al.: Real-time license plate recognition on an embedded DSP-platform. In: Computer Vision Pattern Recognition, pp. 1–8, June 2007Google Scholar
  30. 30.
    Frei, W., et al.: Fast boundary detection: a generalization and new algorithm. IEEE Trans. Comput. C-26, 988–998 (1977)CrossRefGoogle Scholar
  31. 31.
    Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986)CrossRefGoogle Scholar
  32. 32.
    Pratt, W.K.: Digital Image Processing, pp. 491–556. Wiley, Hoboken (1991)zbMATHGoogle Scholar
  33. 33.
    Azad, R., et al.: Real-time and efficient method for accuracy enhancement of edge based license plate recognition system. In: International Conf. on computer, Information Technology and Digital Media (2013)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  • Yaecob Girmay Gezahegn
    • 1
  • Misgina Tsighe Hagos
    • 2
  • Dereje H. Mariam W. Gebreal
    • 1
  • Zeferu Teklay Gebreslassie
    • 2
  • G. agziabher Ngusse G. Tekle
    • 3
  • Yakob Kiros T. Haimanot
    • 3
  1. 1.Addis Ababa UniversityAddis AbabaEthiopia
  2. 2.Ethiopian Biotechnology InstituteAddis AbabaEthiopia
  3. 3.Mekelle UniversityMekelleEthiopia

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