A license plate-recognition algorithm for intelligent transportation system applications

Anagnostopoulos, CNE; Anagnostopoulos, IE; Loumos, V; Kayafas, E

dc.contributor.author	Anagnostopoulos, CNE	en
dc.contributor.author	Anagnostopoulos, IE	en
dc.contributor.author	Loumos, V	en
dc.contributor.author	Kayafas, E	en
dc.date.accessioned	2014-03-01T01:23:24Z
dc.date.available	2014-03-01T01:23:24Z
dc.date.issued	2006	en
dc.identifier.issn	1524-9050	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/16954
dc.subject	Image processing	en
dc.subject	License plate recognition (LPR)	en
dc.subject	Optical character recognition (OCR)	en
dc.subject	Probabilistic neural network (PNN)	en
dc.subject.classification	Engineering, Civil	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.classification	Transportation Science & Technology	en
dc.subject.other	Alphanumeric characters	en
dc.subject.other	Character recognition neural network	en
dc.subject.other	License plate recognition (LPR)	en
dc.subject.other	Probabilistic neural network (PNN)	en
dc.subject.other	Algorithms	en
dc.subject.other	Cameras	en
dc.subject.other	Image processing	en
dc.subject.other	Image segmentation	en
dc.subject.other	Intelligent agents	en
dc.subject.other	Lighting	en
dc.subject.other	Neural networks	en
dc.subject.other	Optical character recognition	en
dc.subject.other	License plates (automobile)	en
dc.title	A license plate-recognition algorithm for intelligent transportation system applications	en
heal.type	journalArticle	en
heal.identifier.primary	10.1109/TITS.2006.880641	en
heal.identifier.secondary	http://dx.doi.org/10.1109/TITS.2006.880641	en
heal.identifier.secondary	1688109	en
heal.language	English	en
heal.publicationDate	2006	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 concentric windows) and connected component analysis in conjunction with a character recognition neural network. The algorithm was tested with 1334 natural-scene gray-level vehicle images of different backgrounds and ambient illumination. The camera focused in 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 1287 over 1334 input images (96.5%). The optical character recognition system is a two-layer probabilistic neural network (PNN) with topology 108-180-36, whose performance for entire plate recognition reached 89.1%. The PNN is trained to identify alphanumeric characters from car license plates based on data obtained from algorithmic image processing. Combining the above two rates, the overall rate of success for the license-plate-recognition algorithm is 86.0%. A review in the related literature presented in this paper reveals that better performance (90% up to 95%) has been reported, when limitations in distance, angle of view, illumination conditions are set, and background complexity is low. © 2006 IEEE.	en
heal.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC	en
heal.journalName	IEEE Transactions on Intelligent Transportation Systems	en
dc.identifier.doi	10.1109/TITS.2006.880641	en
dc.identifier.isi	ISI:000240376500010	en
dc.identifier.volume	7	en
dc.identifier.issue	3	en
dc.identifier.spage	377	en
dc.identifier.epage	391	en