TY  - JOUR
PY  - 2021//
TI  - Deep-learning-based approach for Iraqi and Malaysian vehicle license plate recognition
JO  - Computational intelligence and neuroscience
A1  - Habeeb, Dhuha
A1  - Noman, Fuad
A1  - Alkahtani, Ammar Ahmed
A1  - Alsariera, Yazan A.
A1  - Alkawsi, Gamal
A1  - Fazea, Yousef
A1  - Al-Jubari, Ammar Mohammed
SP  - e3971834
EP  - e3971834
VL  - 2021
IS  - 
N2  - 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.<p />  <p>Language: en</p>
LA  - en
SN  - 1687-5265
UR  - http://dx.doi.org/10.1155/2021/3971834
ID  - ref1
ER  -