Multi-threshold Approach for License Plate Recognition System

The objective of this paper is to propose an adaptive multi threshold for image segmentation precisely in object detection. Due to the different types of license plates being used, the requirement of an automatic LPR is rather different for each country. The proposed technique is applied on Malaysian LPR application. It is based on Multi Layer Perceptron trained by back propagation. The proposed adaptive threshold is introduced to find the optimum threshold values. The technique relies on the peak value from the graph of the number object versus specific range of threshold values. The proposed approach has improved the overall performance compared to current optimal threshold techniques. Further improvement on this method is in progress to accommodate real time system specification.

• Siti Norul Huda Sheikh Abdullah
• Farshid Pirahan Siah
• Nor Hanisah Haji Zainal Abidin
• Shahnorbanun Sahran

• Multi-threshold approach
• license plate recognition system.

[1] C.-I. Chang, Y. Du, J. Wang, S.-M. Guo, and P. Thouin, "Survey
and comparative analysis of entropy and relative entropy thresholding
techniques," Vision, Image and Signal Processing, IEE Proceedings -,
vol. 153, no. 6, pp. 837 -850, dec. 2006.
[2] J. Acharya and G. Sreechakra, "A novel electrostatics based image binarization
technique," in International Conference Systemics, Cybernetics
and Informatics ( ICSCI - 2007). Andhra Pradesh, India: Pentagram
Research, January 03 - 07 2007.
[3] S. N. H. S. Abdullah, M. Khalid, R. Yusof, and K. Omar, "License
plate recognition based on geometry features topological analysis and
support vector machine," in Proceedings of Malaysia-Japan International
Symposium On Advanced Technology (MJISAT2007), Kuala Lumpur,
Malaysia, 12th-15 November 2007, cD Proceeding.
[4] ÔÇöÔÇö, "Comparison of feature extractors in license plate recognition," in
IEEE Proceedings of First Asia International Conference on Modelling
& Simulation (AMS2007), Phuket, Thailand, 2007.
[5] N. Otsu, "A threshold selection method from gray level histograms,"
IEEE Trans. Systems, Man and Cybernetics, vol. 9, pp. 62-66, Mar. 1979,
minimize inter class variance.
[6] J. Nijhuis, M. Brugge, and K. Helmholt, "License plate recognition using
dtcnns." in Proceedings 1998 Fifth IEEE International Workshop on
Publish Security Technology, 1997., 1998, pp. 212-217.