Abstract
Vehicle License Plate Recognition (LPR) has become a crucial system for various applications such as security monitoring, parking access, law enforcement and so on. LPR is employed for the identification of vehicles using their license plate. Recently, LPR technology has evolved progressively where vast improvement had been made towards the development of the image recognition’s quality and speed, as well as its state of the art methods.
Although several research studies managed to resolve most of the issues that arise in LPR systems, more studies need to be conducted to improve the performance of LPR. This paper aims to provide a comprehensive analysis and comparison of different methods used in LPR. It summarizes each of the methods in terms of their accuracy, performance, strengths and weaknesses. Based on the recognition techniques used, LPR is then characterized into two categories, namely Traditional Computer Vision and Deep Learning Techniques.
Keywords: license plate recognition, LPR, deep learning, computer vision.
Cite
@article{abdullah2021license,
title = {{License Plate Recognition Techniques: Comparative Study}},
author = {Abdullah, Munaisyah and Al-Nawah, Salah Mohammed and Osman, Husna and Jaffar, Jasrina},
year = {2021},
journal = {Malaysian Journal of Computer Science},
volume = {Special Issue 1/2021 (Advances in Applied Science and Technology Research)},
pages = {94--105},
doi = {10.22452/mjcs.sp2021no1.9},
url = {https://doi.org/10.22452/mjcs.sp2021no1.9}
}