Segmentation of characters on car license plates

He, X; Zheng, L; Wu, Q; Jia, W; Samali, B; Palaniswami, M

Segmentation of characters on car license plates

He, X

Zheng, L Wu, Q

Jia, W

Samali, B

Palaniswami, M

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2008 IEEE 10th Workshop on Multimedia Signal Processing, MMSP 2008, 2008, pp. 399 - 402
Issue Date:: 2008-12-31

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Field	Value	Language
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.contributor.author	Zheng, L	en_US
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483	en_US
dc.contributor.author	Jia, W https://orcid.org/0000-0002-0940-3338	en_US
dc.contributor.author	Samali, B https://orcid.org/0000-0002-3426-7745	en_US
dc.contributor.author	Palaniswami, M	en_US
dc.date.issued	2008-12-31	en_US
dc.identifier.citation	Proceedings of the 2008 IEEE 10th Workshop on Multimedia Signal Processing, MMSP 2008, 2008, pp. 399 - 402	en_US
dc.identifier.isbn	9781424422951	en_US
dc.identifier.uri	http://hdl.handle.net/10453/10926
dc.description.abstract	License plate recognition usually contains three steps, namely license plate detection/localization, character segmentation and character recognition. When reading characters on a license plate one by one after license plate detection step, it is crucial to accurately segment the characters. The segmentation step may be affected by many factors such as license plate boundaries (frames). The recognition accuracy will be significantly reduced if the characters are not properly segmented. This paper presents an efficient algorithm for character segmentation on a license plate. The algorithm follows the step that detects the license plates using an AdaBoost algorithm. It is based on an efficient and accurate skew and slant correction of license plates, and works together with boundary (frame) removal of license plates. The algorithm is efficient and can be applied in real-time applications. The experiments are performed to show the accuracy of segmentation. © 2008 IEEE.	en_US
dc.relation.ispartof	Proceedings of the 2008 IEEE 10th Workshop on Multimedia Signal Processing, MMSP 2008	en_US
dc.relation.isbasedon	10.1109/MMSP.2008.4665111	en_US
dc.title	Segmentation of characters on car license plates	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Cairns, Australia	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
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

License plate recognition usually contains three steps, namely license plate detection/localization, character segmentation and character recognition. When reading characters on a license plate one by one after license plate detection step, it is crucial to accurately segment the characters. The segmentation step may be affected by many factors such as license plate boundaries (frames). The recognition accuracy will be significantly reduced if the characters are not properly segmented. This paper presents an efficient algorithm for character segmentation on a license plate. The algorithm follows the step that detects the license plates using an AdaBoost algorithm. It is based on an efficient and accurate skew and slant correction of license plates, and works together with boundary (frame) removal of license plates. The algorithm is efficient and can be applied in real-time applications. The experiments are performed to show the accuracy of segmentation. © 2008 IEEE.

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http://hdl.handle.net/10453/10926