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A Survey on License Plate Recognition Systems

by Divya Gilly, Kumudha Raimond
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 61 - Number 6
Year of Publication: 2013
Authors: Divya Gilly, Kumudha Raimond
10.5120/9934-4569

Divya Gilly, Kumudha Raimond . A Survey on License Plate Recognition Systems. International Journal of Computer Applications. 61, 6 ( January 2013), 34-40. DOI=10.5120/9934-4569

@article{ 10.5120/9934-4569,
author = { Divya Gilly, Kumudha Raimond },
title = { A Survey on License Plate Recognition Systems },
journal = { International Journal of Computer Applications },
issue_date = { January 2013 },
volume = { 61 },
number = { 6 },
month = { January },
year = { 2013 },
issn = { 0975-8887 },
pages = { 34-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume61/number6/9934-4569/ },
doi = { 10.5120/9934-4569 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:08:23.521448+05:30
%A Divya Gilly
%A Kumudha Raimond
%T A Survey on License Plate Recognition Systems
%J International Journal of Computer Applications
%@ 0975-8887
%V 61
%N 6
%P 34-40
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

License Plate Recognition (LPR) is a well known image processing technology. LPR system consists of four steps: capture the image from digital camera, pre-processing, character segmentation and character recognition. License plates are available in various styles and colors in various countries. Every country has their own license plate format. So each country develops the LPR system appropriate for the vehicle license plate format. Difficulties that the LPR systems face are the environmental and non-uniform outdoor illumination conditions. Therefore, most of the systems work under restricted environmental conditions like fixed illumination, limited vehicle speed, designated routes, and stationary backgrounds. Each LPR system use different combination of algorithms. From the papers being surveyed, it is realized that a good success rate of 93. 7% is obtained by the combination of fuzzy logic for license plate detection and Self Organizing (SO) neural network for character recognition. Comparisons of different LPR systems are discussed in this paper.

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Index Terms

Computer Science
Information Sciences

Keywords

Image processing License Plate Recognition License Plate Detection License Plate Segmentation Optical Character Recognition

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