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Using sliding concentric windows for license plate segmentation and processing

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dc.contributor.author Anagnostopoulos, C en
dc.contributor.author Anagnostopoulos, I en
dc.contributor.author Tsekouras, G en
dc.contributor.author Kouzas, G en
dc.contributor.author Loumos, V en
dc.contributor.author Kayafas, E en
dc.date.accessioned 2014-03-01T02:43:45Z
dc.date.available 2014-03-01T02:43:45Z
dc.date.issued 2005 en
dc.identifier.issn 15206130 en
dc.identifier.uri http://hdl.handle.net/123456789/31501
dc.subject Character Recognition en
dc.subject Image Processing en
dc.subject Image Segmentation en
dc.subject Natural Scenes en
dc.subject Optical Character Recognition en
dc.subject Probabilistic Neural Network en
dc.subject Sliding Window en
dc.subject Neural Network en
dc.subject.other Algorithms en
dc.subject.other Cameras en
dc.subject.other License plates (automobile) en
dc.subject.other Lighting en
dc.subject.other Neural networks en
dc.subject.other Optical character recognition en
dc.subject.other Pattern matching en
dc.subject.other Topology en
dc.subject.other Algorithmic image processing en
dc.subject.other Multi font alphanumeric characters en
dc.subject.other Probabilistic Neural Network en
dc.subject.other Sliding concentric windows en
dc.subject.other Image segmentation en
dc.title Using sliding concentric windows for license plate segmentation and processing en
heal.type conferenceItem en
heal.identifier.primary 10.1109/SIPS.2005.1579889 en
heal.identifier.secondary http://dx.doi.org/10.1109/SIPS.2005.1579889 en
heal.identifier.secondary 1579889 en
heal.publicationDate 2005 en
heal.abstract In this paper, a new algorithm for vehicle license plate identification is proposed, on the basis of a novel adaptive image segmentation technique (Sliding Windows) in conjunction with a character recognition Neural Network. The algorithm was tested with 2820 natural scene gray level vehicle images of different backgrounds and ambient illumination. The camera focused on the plate, while the angle of view and the distance from the vehicle varied according to the experimental setup. The license plates properly segmented were 2719 over 2820 input images (96.4%). The Optical Character Recognition (OCR) system is a two layer Probabilistic Neural Network with topology 108-180-36, whose performance reached 97.4%. The PNN was trained to identify multi-font alphanumeric characters from car license plates based on data obtained from algorithmic image processing. © 2005 IEEE. en
heal.journalName IEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation en
dc.identifier.doi 10.1109/SIPS.2005.1579889 en
dc.identifier.volume 2005 en
dc.identifier.spage 337 en
dc.identifier.epage 342 en


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