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First Asia International Conference on Modelling & Simulation (AMS'07)
Comparison of Feature Extractors in License Plate Recognition
Prince of Songkla University, Phuket, Thailand
March 27-March 30
ISBN: 0-7695-2845-7
Siti Norul Huda Sheikh Abdullah, Universiti Teknologi Malaysia
Marzuki Khalid, Universiti Teknologi Malaysia
Rubiyah Yusof, Universiti Teknologi Malaysia
Khairuddin Omar, Universiti Kebangsaan Malaysia,
Vehicle license plate recognition has been intensively studied in many countries. Due to the different types of license plates being used, the requirement of an automatic license plate recognition system is different for each country. In this paper, an automatic license plate recognition system is proposed for Malaysian vehicles with standard license plates using blob labeling and clustering for segmentation, seven popular and one proposed edge detectors for feature extraction and neural networks for classification.There were eight experiments conducted using eight different edge dectectors: Kirsch, Sobel, Laplacian, Wallis, Prewitt, Frei Chen and a proposed edge detector. The result had shown kirsch edge detectors is the best technique for feature exractor while the proposed achieved better results compared to Prewitt, Frei Chen and Wallis.
Citation:
Siti Norul Huda Sheikh Abdullah, Marzuki Khalid, Rubiyah Yusof, Khairuddin Omar, "Comparison of Feature Extractors in License Plate Recognition," ams, pp.502-506, First Asia International Conference on Modelling & Simulation (AMS'07), 2007
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