International Journal of Computer Techniques Volume 12 Issue 3 | Automatic Number Plate Recognition using YOLOv11
Automatic Number Plate Recognition using YOLOv11
International Journal of Computer Techniques – Volume 12 Issue 3, May – June 2025
Abstract
This study presents a **state-of-the-art Automatic Number Plate Recognition (ANPR) system** leveraging the **YOLOv11 object detection model** with **PaddleOCR** for precise alphanumeric extraction. Trained on a **custom dataset enhanced with COCO images**, the system ensures **real-time detection accuracy of up to 98%**, making it highly applicable for **traffic surveillance, toll collection, parking management, and law enforcement operations**.
Keywords
ANPR, YOLOv11, AI-powered number plate recognition, deep learning, computer vision, PaddleOCR, traffic surveillance, vehicle monitoring.
Conclusion
The **YOLOv11-powered ANPR system** significantly enhances real-time **vehicle identification** by combining **advanced deep learning techniques with OCR-based content extraction**. The modular architecture ensures **scalability, cost-effectiveness, and adaptability**, making it an ideal solution for **intelligent transportation systems**. Future enhancements include **multi-language plate recognition, better handling of low-resolution images, and cloud-based traffic analytics integration**.
References
- Lubna, Mufti, N., & Shah, S. A. A. (2021). “Automatic Number Plate Recognition: A Detailed Survey of Relevant Algorithms.” Sensors.
- Mehra, S., & Garg, D. (2025). “AI-Based Framework for Automatic Vehicle Number Plate Detection.” Engineering, Technology & Applied Science Research.
- Sutikno, T., Nurwulandari, D. A., Huda, A., & Supangkat, S. H. (2025). “Enhanced Automatic License Plate Detection using CLAHE and YOLOv11.” CRC Press.
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