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License plate detection and segmentation using cluster run length smoothing algorithm

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 a...

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Main Authors: Abdullah, S. N. H. S., Sudin, M. N., Prabuwono, A. S., Mantoro, T.
Format: Article
Language:English
Published: 2012
Subjects:
T Technology
Online Access:http://eprints.utm.my/id/eprint/47158/1/TeddyMantoro2012_LicensePlateDetectionandSegmentation%281%29.pdf
http://eprints.utm.my/id/eprint/47158/
https://doi.org/10.4018/jitr.2012070103
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spelling my.utm.471582020-02-29T13:07:21Z http://eprints.utm.my/id/eprint/47158/ License plate detection and segmentation using cluster run length smoothing algorithm Abdullah, S. N. H. S. Sudin, M. N. Prabuwono, A. S. Mantoro, T. T Technology 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 diffi-culty. 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 3 × 3 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%. 2012 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/47158/1/TeddyMantoro2012_LicensePlateDetectionandSegmentation%281%29.pdf Abdullah, S. N. H. S. and Sudin, M. N. and Prabuwono, A. S. and Mantoro, T. (2012) License plate detection and segmentation using cluster run length smoothing algorithm. Journal of Information Technology Research, 5 (3). pp. 46-70. ISSN 1938-7857 https://doi.org/10.4018/jitr.2012070103 DOI:10.4018/jitr.2012070103
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic T Technology
spellingShingle T Technology
Abdullah, S. N. H. S.
Sudin, M. N.
Prabuwono, A. S.
Mantoro, T.
License plate detection and segmentation using cluster run length smoothing algorithm
description 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 diffi-culty. 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 3 × 3 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%.
format Article
author Abdullah, S. N. H. S.
Sudin, M. N.
Prabuwono, A. S.
Mantoro, T.
author_facet Abdullah, S. N. H. S.
Sudin, M. N.
Prabuwono, A. S.
Mantoro, T.
author_sort Abdullah, S. N. H. S.
title License plate detection and segmentation using cluster run length smoothing algorithm
title_short License plate detection and segmentation using cluster run length smoothing algorithm
title_full License plate detection and segmentation using cluster run length smoothing algorithm
title_fullStr License plate detection and segmentation using cluster run length smoothing algorithm
title_full_unstemmed License plate detection and segmentation using cluster run length smoothing algorithm
title_sort license plate detection and segmentation using cluster run length smoothing algorithm
publishDate 2012
url http://eprints.utm.my/id/eprint/47158/1/TeddyMantoro2012_LicensePlateDetectionandSegmentation%281%29.pdf
http://eprints.utm.my/id/eprint/47158/
https://doi.org/10.4018/jitr.2012070103
_version_ 1662754247627767808
score 13.223943

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