Citation

Lim, Zheng You and Bong, Zi Jun and Pang, Ying Han and Ooi, Shih Yin and Khoh, Wee How and Hiew, Fu San (2025) Integration of Yolov8 and Paddleocr for car plate recognition system in diverse environments: Malaysia. Journal of Engineering Science and Technology., 20 (1). pp. 250-270. ISSN 1823-4690

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Abstract

An efficient car plate recognition system is significant for traffic management. However, diverse environmental factors such as poor illumination and different weather conditions, may pose challenges to car plate recognition. The efficiency of object detection and character recognition serves as a route map toward accomplishing desired outcomes within these constraints. This study devises a car plate recognition system by integrating the new variant of You-Only-Look-Once, i.e. YOLOv8, for car plate detection and PaddleOCR for extraction and recognition of the detected car plate. A self-collected database containing more than 600 car plates is collected and employed to assess the efficacy of the developed system. In this study, comprehensive experiments are carried out to assess the effectiveness of the developed system under different environmental conditions in Malaysia, including normal (day), nighttime, and rainy conditions. The empirical results exhibit the superior performance of the proposed system across diverse conditions. In conclusion, we can deduce that the proposed system is able to demonstrate robustness and reliability in car plate recognition, even under challenging environmental conditions. The integration of YOLOv8 and PaddleOCR in the car plate recognition system allows efficient detection and recognition of car plates, effectively dealing with occlusions and complex backgrounds. In future work, the system will be tested under a broader array of environmental conditions encountered in other countries, including foggy, dusty, snowfall environments, etc., to fortify its robustness in real-world scenarios. © 2025 Taylor's University. All rights reserved.

Item Type:	Article
Uncontrolled Keywords:	Artificial intelligence, Car plate recognition
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	18 Feb 2025 04:39
Last Modified:	18 Feb 2025 09:17
URII:	http://shdl.mmu.edu.my/id/eprint/13488

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