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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1330677Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2172
 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.
 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.
 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.
 ÔÇöÔÇö, "Comparison of feature extractors in license plate recognition," in IEEE Proceedings of First Asia International Conference on Modelling & Simulation (AMS2007), Phuket, Thailand, 2007.
 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.
 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.