Publication: Deep-Learning-Based Approach for Iraqi and Malaysian Vehicle License Plate Recognition
Date
2021
Authors
Habeeb D.
Noman F.
Alkahtani A.A.
Alsariera Y.A.
Alkawsi G.
Fazea Y.
Al-Jubari A.M.
Journal Title
Journal ISSN
Volume Title
Publisher
Hindawi Limited
Abstract
Recognizing vehicle plate numbers is a key step towards implementing the legislation on traffic and reducing the number of daily traffic accidents. Although machine learning has advanced considerably, the recognition of license plates remains an obstacle, particularly in countries whose plate numbers are written in different languages or blended with Latin alphabets. This paper introduces a recognition system for Arabic and Latin alphabet license plates using a deep-learning-based approach in conjugation with data collected from two specific countries: Iraq and Malaysia. The system under study is proposed to detect, segment, and recognize vehicle plate numbers. Moreover, Iraqi and Malaysian plates were used to compare these processes. A total of 404 Iraqi images and 681 Malaysian images were tested and used for the proposed techniques. The evaluation took place under various atmospheric environments, including fog, different contrasts, dirt, different colours, and distortion problems. The proposed approach showed an average recognition rate of 85.56% and 88.86% on Iraqi and Malaysian datasets, respectively. Thus, this evidences that the deep-learning-based method outperforms other state-of-the-art methods as it can successfully detect plate numbers regardless of the deterioration level of image quality. Copyright � 2021 Dhuha Habeeb et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Description
Accidents; Deep learning; Deterioration; Optical character recognition; Atmospheric environment; Learning-based approach; Learning-based methods; Malaysia; Malaysians; Recognition systems; State-of-the-art methods; Vehicle license plate recognition; License plates (automobile); machine learning; traffic accident; Accidents, Traffic; Deep Learning; Machine Learning