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License Plate Detection and Segmentation Using Cluster Run Length Smoothing Algorithm

License Plate Detection and Segmentation Using Cluster Run Length Smoothing Algorithm

Siti Norul Huda Sheikh Abdullah, Muhammad Nuruddin Sudin, Anton Satria Prabuwono, Teddy Mantoro
Copyright: © 2012 |Volume: 5 |Issue: 3 |Pages: 25
ISSN: 1938-7857|EISSN: 1938-7865|EISBN13: 9781466615038|DOI: 10.4018/jitr.2012070103
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MLA

Abdullah, Siti Norul Huda Sheikh, et al. "License Plate Detection and Segmentation Using Cluster Run Length Smoothing Algorithm." JITR vol.5, no.3 2012: pp.46-70. http://doi.org/10.4018/jitr.2012070103

APA

Abdullah, S. N., Sudin, M. N., Prabuwono, A. S., & Mantoro, T. (2012). License Plate Detection and Segmentation Using Cluster Run Length Smoothing Algorithm. Journal of Information Technology Research (JITR), 5(3), 46-70. http://doi.org/10.4018/jitr.2012070103

Chicago

Abdullah, Siti Norul Huda Sheikh, et al. "License Plate Detection and Segmentation Using Cluster Run Length Smoothing Algorithm," Journal of Information Technology Research (JITR) 5, no.3: 46-70. http://doi.org/10.4018/jitr.2012070103

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Abstract

For 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 detection system is proposed for Malaysian vehicles with standard license plates based on image processing and clustering. Detecting the location of license plate is a vital issue when dealing with uncontrolled environments and illumination difficulty. Therefore, a proposed algorithm called Cluster Run Length Smoothing Algorithm (CRLSA) was applied to locate the license plates at the right position. CRLSA consisted of two separate proposed algorithms which applied run length edge detector algorithm using kernel masks and 128 grayscale offset plus a three-dimensional way to calculate run length smoothing algorithm, which can improve clustering techniques in segmentation phase. Six separate experiments were performed; Morphology, CRLSA, Clustering, Square/Contour Detection, Hough, and Radon Transform. From those experiments, analysis based on segmentation errors was constructed. The prototyped system has accuracy more than 96%.

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