Two Neural Networks for License Number Plates Recognition

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TitreTwo Neural Networks for License Number Plates Recognition
Type de publicationArticle de revue
AuteurAkoum, Al Hussain, Daya, Bassam, Chauvet, Pierre
EditeurAsian Research Publication Network
TypeArticle scientifique dans une revue à comité de lecture
Année2010
LangueAnglais
Date2010
Numéro1
Pagination25 - 32
Volume12
Titre de la revueJournal of Theoretical and Applied Information Technology
Mots-clésArtificial neural network, Character recognition, feature extraction, Image processing, license number identification, license plate locating, segmentation
Résumé en anglais

A license plate recognition system is an automatic system that is able to recognize a license plate number, extracted from an image device. Such system is useful in many fields and places: parking lots, private and public entrances, border control, theft and vandalism control. In our paper we designed such a system. First we separated each digit from the license plate using image processing tools. Then we built a classifier, using a training set based on digits extracted from approximately 350 license plates. Once a license plate is detected, its digits are recognized, displayed on the User Interface or checked against a database. The focus is on the design of algorithms used for extracting the license plate from an image of the vehicle, isolating the characters of the plate and identifying characters. Our approach is considered to identify vehicle through recognizing of its license plate using two different types of neural networks: Hopfield and the multi layer perceptron "MLP". A comparative result has shown the ability to recognize the license plate successfully. The experimental results have shown the ability of Hopfield Network to recognize correctly characters on license plate more than MLP architecture which has a weaker performance. A negative point in the case of Hopfield is the processing time.

URL de la noticehttp://okina.univ-angers.fr/publications/ua1364
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http://www.jatit.org/volumes/research-papers/Vol12No1/5Vol12No1.pdf