An algorithm for accuracy enhancement of license plate recognition

Zheng, L; He, X; Samali, B; Yang, LT

An algorithm for accuracy enhancement of license plate recognition

Zheng, L

He, X

Samali, B

Yang, LT

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Publication Type:: Journal Article
Citation:: Journal of Computer and System Sciences, 2013, 79 (2), pp. 245 - 255
Issue Date:: 2013-03-01

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Field	Value	Language
dc.contributor.author	Zheng, L https://orcid.org/0000-0001-5728-4356	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.contributor.author	Samali, B https://orcid.org/0000-0002-3426-7745	en_US
dc.contributor.author	Yang, LT	en_US
dc.date.issued	2013-03-01	en_US
dc.identifier.citation	Journal of Computer and System Sciences, 2013, 79 (2), pp. 245 - 255	en_US
dc.identifier.issn	0022-0000	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26309
dc.identifier.uri	http://hdl.handle.net/10453/34146
dc.description.abstract	This paper presents an algorithm for extraction (detection) and recognition of license plates in traffic video datasets. For license plate detection, we introduce a method that applies both global edge features and local Haar-like features to construct a cascaded classifier consisting of 6 layers with 160 features. The characters on a license plate image are extracted by a method based on an improved blob detection algorithm for removal of unwanted areas. For license plate recognition (i.e., character recognition), an open source OCR is modified and used. Our proposed system is robust under poor illumination conditions and for moving vehicles. Our overall system is efficient and can be applied in real-time applications. Experimental results are demonstrated using a traffic video. © 2012 Elsevier Inc.	en_US
dc.relation.ispartof	Journal of Computer and System Sciences	en_US
dc.relation.isbasedon	10.1016/j.jcss.2012.05.006	en_US
dc.subject.classification	Computation Theory & Mathematics	en_US
dc.title	An algorithm for accuracy enhancement of license plate recognition	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	79	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0806 Information Systems	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	closed_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	79	en_US

Abstract:

This paper presents an algorithm for extraction (detection) and recognition of license plates in traffic video datasets. For license plate detection, we introduce a method that applies both global edge features and local Haar-like features to construct a cascaded classifier consisting of 6 layers with 160 features. The characters on a license plate image are extracted by a method based on an improved blob detection algorithm for removal of unwanted areas. For license plate recognition (i.e., character recognition), an open source OCR is modified and used. Our proposed system is robust under poor illumination conditions and for moving vehicles. Our overall system is efficient and can be applied in real-time applications. Experimental results are demonstrated using a traffic video. © 2012 Elsevier Inc.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/26309