Please use this identifier to cite or link to this item: http://localhost:8080/xmlui/handle/123456789/110
Title: Hybrid Proposed Model for Automatic License Plate Recognition and Distinction (ALPRD)
Authors: Abdulbaqi, Azmi
Mosslah, Abd
Keywords: Artificial Neural Networks
Feature Extraction
Genetic Algorithm, NNGA
Issue Date: Jun-2016
Publisher: Journal of Information, Communication
Abstract: This paper provides the overview of proposed Hybrid Model to construct a method to distinction between all types of cars plate's numbers. Plate's distinction system (PDS) for public, private and governmental cars plates' numbers identification and verification by using neural networks and genetic algorithm (NNGA) is proposed. This proposed algorithm demonstrated its efficiency and accuracy through the satisfactory results. It was proved a higher performance of the results. The model consists of three phases. The first phase applied the pre-processing over the plate number images. The second phase is extract the features of inputted plate, which will be passes as nodes of neural network .The third phase is pass the result of neural network to the genetic algorithm and then classify the output of cars plates numbers as private or public and governmental plates .
URI: http://localhost:8080/xmlui/handle/123456789/110
ISSN: 19948638/26640600
Appears in Collections:قسم التفسير وعلوم القرأن

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