Accuracy Enhancement for License Plate Recognition

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dc.contributor.author Zheng, L
dc.contributor.author He, S
dc.contributor.author Samali, B
dc.contributor.author Yang, L
dc.contributor.editor Lihong, Z
dc.contributor.editor Xiangjian, H
dc.contributor.editor Samali, B
dc.contributor.editor Yang, LT
dc.date.accessioned 2012-02-02T11:08:09Z
dc.date.issued 2010-01
dc.identifier.citation Proceedings - 10th IEEE International Conference on Computer and Information Technology (CIT 2010), 2010, pp. 511 - 516
dc.identifier.isbn 978-0-7695-4108-2
dc.identifier.other E1 en_US
dc.identifier.uri http://hdl.handle.net/10453/16285
dc.description.abstract Automatic License Plate Recognition is useful for real time traffice management and surveillance. License plate recognition usually contains two steps, namely license plate detection/localization and character recognition. Recognizing character in a license plate is very difficult task due to poor illumination conditions and rapid motion of vehicles. When using an OCR for character recognition, it is crucial to correctly remove the license plate boundaries after the step for license plate detection. No matter which OCRs are used, the recognition accuracy will be significantly reduced if the boundaries are not properly removed. This paper presents an efficient algorithm for non character area removal. The algorithm is based on the license plates detected using an AdaBoost algorithm. Then it follows the steps of character height estimation, character width estimation, segmentation and block identification. The algorithm is efficient and can be applied in real time applications. The experiments are performed using OCR software for character recognition. It is shown that much higher recognition accuracy is obtained by gradually removing the license plate boundaries
dc.publisher IEEE Computer Society
dc.relation.isbasedon 10.1109/CIT.2010.111
dc.title Accuracy Enhancement for License Plate Recognition
dc.type Conference Proceeding
dc.parent Proceedings - 10th IEEE International Conference on Computer and Information Technology (CIT 2010)
dc.journal.number en_US
dc.publocation Bradford, West Yorkshire UK en_US
dc.identifier.startpage 511 en_US
dc.identifier.endpage 516 en_US
dc.cauo.name FEIT.Faculty of Engineering & Information Technology en_US
dc.conference Verified OK en_US
dc.conference IEEE International Conference on Computer and Information Technology
dc.for 0806 Information Systems
dc.personcode 990421
dc.personcode 870186
dc.personcode 10144691
dc.percentage 100 en_US
dc.classification.name Information Systems en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom IEEE International Conference on Computer and Information Technology en_US
dc.date.activity 20100629 en_US
dc.date.activity 2010-06-29
dc.location.activity Bradford, West Yorkshire UK en_US
dc.description.keywords LPR, image segmentation, blob extraction, CCA en_US
pubs.embargo.period Not known
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 Computing and Communications
pubs.organisational-group /University of Technology Sydney/Strength - Built Infrastructure
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10
utslib.collection.history School of Computing and Communications (ID: 335)
utslib.collection.history Closed (ID: 3)


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