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dc.contributor.authorZhai, Xiaojun
dc.contributor.authorBensaali, Faycal
dc.contributor.authorSotudeh, Reza
dc.date.accessioned2013-01-10T11:29:08Z
dc.date.available2013-01-10T11:29:08Z
dc.date.issued2012
dc.identifier.citationZhai , X , Bensaali , F & Sotudeh , R 2012 , OCR-based neural network for ANPR . in Procs 2012 IEEE Int Conf on Imaging Systems and Techniques IST) . IEEE , pp. 393-397 , Imaging Systems and Techniques (IST), 2012 IEEE International Conference on , Manchester , United Kingdom , 16/07/12 . https://doi.org/10.1109/IST.2012.6295581
dc.identifier.citationconference
dc.identifier.isbn978-1-4577-1776-5
dc.identifier.otherPURE: 1043381
dc.identifier.otherPURE UUID: 9d4da9c2-12c7-4a29-9284-1099144c62fd
dc.identifier.otherScopus: 84870680419
dc.identifier.urihttp://hdl.handle.net/2299/9540
dc.description.abstractOptical Character Recognition (OCR) is the last stage in an Automatic Number Plate Recognition System (ANPRs). In this stage the number plate characters on the number plate image are converted into encoded texts. In this paper, an Artificial Neural Network (ANN) based OCR algorithm for ANPR application is presented. A database of 3700 UK binary character images have been used for testing the performance of the proposed algorithm. Results achieved have shown that the proposed algorithm can meet the real-time requirement of an ANPR system and can averagely process a character image in 8.4ms with 97.3% successful recognition rateen
dc.language.isoeng
dc.publisherIEEE
dc.relation.ispartofProcs 2012 IEEE Int Conf on Imaging Systems and Techniques IST)
dc.titleOCR-based neural network for ANPRen
dc.contributor.institutionSchool of Engineering and Technology
dc.contributor.institutionScience & Technology Research Institute
dc.contributor.institutionCentre for Engineering Research
dc.contributor.institutionDigital Media Processing and Biometrics
dc.contributor.institutionSmart Electronics Devices and Networks
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=84870680419&partnerID=8YFLogxK
rioxxterms.versionAM
rioxxterms.versionofrecordhttps://doi.org/10.1109/IST.2012.6295581
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue


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