An efficient automated vehicle license plate recognition system under image processing

An automated vehicle license plate recognition system using image processing techniques identifies vehicle numbers without human interference. This system has significant impact because of its good application in various fields like car parking, access control, speed control, automatic toll collecti...

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Bibliographic Details
Published inIndonesian Journal of Electrical Engineering and Computer Science Vol. 29; no. 2; p. 1055
Main Authors Islam, Dilshad, Mahmud, Tanjim, Chowdhury, Tanjia
Format Journal Article
LanguageEnglish
Published 01.02.2023
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ISSN2502-4752
2502-4760
DOI10.11591/ijeecs.v29.i2.pp1055-1062

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Summary:An automated vehicle license plate recognition system using image processing techniques identifies vehicle numbers without human interference. This system has significant impact because of its good application in various fields like car parking, access control, speed control, automatic toll collection, border security, traffic violence detection and surveillance applications. This paper presents a methodology that is quite simple but at the same time very much efficient and this system consists of four sequential modules which are preprocessing, number plate extraction, number plate character segmentation and character recognition. Preprocessing aims to improve the image quality that is captured in various illumination conditions and stick out outstanding information that we need, which is favorable to subsequent processing including extraction, segmentation and recognition. After preprocessing various morphological operations are applied to extract the desired license plate region. Then for segmentation the bounding box method is applied that segments each letter and number present on the license plate region. Finally, template matching is applied in identifying all segmented characters present in the license plate image. The experimental results showed that the proposed system can recognize license plate characters efficiently with higher accuracy. Using MATLAB software, the proposed method attains recognition accuracy of 94.17%.
ISSN:2502-4752
2502-4760
DOI:10.11591/ijeecs.v29.i2.pp1055-1062