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This study introduces a real-time Automatic Number Plate Recognition (ANPR) system leveraging advanced YOLO models (v9, v10, v11) for precise license plate detection. The system incorporates state-of-the-art character recognition by comparing Microsoft’s TrOCR and Google’s Tesseract, enabling automated vehicle entry/exit registration and seamless service fee calculation. A streamlined, Streamlit-built user interface ensures efficient operation and integration. Experimental evaluations demonstrate high accuracy and performance, making the system a reliable solution for modern parking management, significantly reducing operational costs and enhancing workforce efficiency.

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Automatic Number Plate Recognition System Using Advanced YOLO and OCR Techniques for Efficient Parking Management

  • 会议论文

  • 发表日期：

    2026年

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  • Thien Van Nguyen,
  • Quan-Manh Nguyen,
  • Thai Dinh Kim,
  • Nam-Ngoc Dao,
  • Phu-Quang Nguyen,
  • Le-Hoang Nguyen

摘要

This study introduces a real-time Automatic Number Plate Recognition (ANPR) system leveraging advanced YOLO models (v9, v10, v11) for precise license plate detection. The system incorporates state-of-the-art character recognition by comparing Microsoft’s TrOCR and Google’s Tesseract, enabling automated vehicle entry/exit registration and seamless service fee calculation. A streamlined, Streamlit-built user interface ensures efficient operation and integration. Experimental evaluations demonstrate high accuracy and performance, making the system a reliable solution for modern parking management, significantly reducing operational costs and enhancing workforce efficiency.