A Deep Learning Based Approach for License Plate Recognition
Article content
Abstract
License plate recognition plays a critical role in modern smart-city applications, traffic monitoring, and law-enforcement systems. Traditional recognition systems often struggle with varying lighting conditions, image blur, occlusion, and differences in Saudi license plate formats. This research proposes an end-to-end deep-learning model for Saudi License Plate Recognition using YOLOv5 for plate detection and a YOLO-based OCR model for character recognition. The dataset was cleaned, verified, re-split, and processed using automated scripts to remove corrupted and blurry images, ensuring high-quality training samples. The YOLOv5 detector achieved strong performance with high precision, recall, and mAP scores across all experiments. Additionally, a custom mapping layer converted English OCR outputs into their equivalent Arabic plate letters, enabling fully bilingual recognition. The final integrated system successfully detects Saudi license plates and extracts the complete alphanumeric sequence with high accuracy. These results demonstrate that deep learning can outperform traditional OCR and rule-based systems, providing a scalable solution for real-world Saudi traffic environments
Keywords
License Plate Recognition, Deep Learning, YOLOv5, Object Detection, Optical Character Recognition, Smart City Applications, Traffic Monitoring, , Bilingual Character Mapping, Computer Vision, Automated Dataset Pre-processing
Downloads
References
G. Jocher, A. Chaurasia, A. Stoken, J. Borovec et al., “YOLOv5: An Ultralytics Open-Source Model for Object Detection,” GitHub Repository, 2023. [Online]. Available: https://github.com/ultralytics/yolov5
Ultralytics, “YOLOv8: Next-Generation Object Detection Model,” Ultralytics Documentation, 2023. [Online]. Available: https://docs.ultralytics.com
Roboflow, “FullCar License Plate Dataset (Saudi LP Dataset),” Roboflow Universe, 2023. [Online]. Available: https://universe.roboflow.com
Kaggle, “License Plate Character Dataset (Plate-Kaggle),” Kaggle Datasets, 2023. [Online]. Available: https://www.kaggle.com
A. Rosebrock, “Laplacian Variance for Blur Detection,” PyImageSearch, 2021. [Online]. Available: https://pyimagesearch.com
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” Proc. CVPR, 2016.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” IEEE TPAMI, vol. 39, no. 6, pp. 1137–1149, 2017.
W. Liu, D. Anguelov, D. Erhan et al., “SSD: Single Shot MultiBox Detector,” ECCV, 2016.
Saudi Standards, Metrology and Quality Organization (SASO), “Saudi Vehicle License Plate Design and Letter Mapping,” Official Documentation, Riyadh, Saudi Arabia, 2020.
OpenCV Developers, “OpenCV Library: Computer Vision and Image Processing,” OpenCV.org, 2023. [Online]. Available: https://opencv.org
Ultralytics, “YOLOv5 Tutorials — Training Custom Data,” Ultralytics Documentation, 2023.
J. Redmon and A. Farhadi, “YOLOv3: An Incremental Improvement,” arXiv:1804.02767, 2018.
J. Redmon and A. Farhadi, “YOLO9000: Better, Faster, Stronger,” CVPR, 2017
Article details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
You must read and accept the copyright terms and conditions(click here for copyright terms).