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<p>In response to rising security concerns in residential and commercial areas, we propose a novel Vehicle Automatic License Plate Recognition (ALPR) and Gate Access System that integrates YOLOv8 for vehicle license plate detection with Tesseract Optical Character Recognition (OCR) for accurate text recognition. This system is designed to function with limited resources and ensures real-time operation. We achieve an impressive accuracy rate of 96% through a combination of YOLOv8’s object detection capabilities and Tesseract’s text extraction. Real-time testing conducted with 52 cars demonstrates that the system correctly identifies 50 vehicles under various lighting conditions. The system integrates an Arduino-based gate control mechanism, which automates vehicle entry by activating a servo motor upon license plate recognition. Additionally, a user-friendly graphical interface built using Streamlit enhances the system’s operability, enabling seamless interaction and monitoring. This research highlights the superior performance of our system compared to existing models, with notable advancements in speed, accuracy, and hardware integration. The results suggest that this system can be deployed in diverse environments, offering reliable and scalable vehicle access control solutions. Our approach represents a significant step toward improving security and automation in real-time vehicle management systems.</p>

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Automatic Vehicle Gate Access Control System Using YOLOv8 and Tesseract Optical Character Recognition

  • 期刊论文

  • 发表日期：

    2026年6月2日

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  • Ashwini Kumar Upadhyay,
  • Archana Verma,
  • Aviral Varshney,
  • Abhinav Rai,
  • Akash Verma,
  • Kartik Rajput,
  • Shivam Singh

摘要

In response to rising security concerns in residential and commercial areas, we propose a novel Vehicle Automatic License Plate Recognition (ALPR) and Gate Access System that integrates YOLOv8 for vehicle license plate detection with Tesseract Optical Character Recognition (OCR) for accurate text recognition. This system is designed to function with limited resources and ensures real-time operation. We achieve an impressive accuracy rate of 96% through a combination of YOLOv8’s object detection capabilities and Tesseract’s text extraction. Real-time testing conducted with 52 cars demonstrates that the system correctly identifies 50 vehicles under various lighting conditions. The system integrates an Arduino-based gate control mechanism, which automates vehicle entry by activating a servo motor upon license plate recognition. Additionally, a user-friendly graphical interface built using Streamlit enhances the system’s operability, enabling seamless interaction and monitoring. This research highlights the superior performance of our system compared to existing models, with notable advancements in speed, accuracy, and hardware integration. The results suggest that this system can be deployed in diverse environments, offering reliable and scalable vehicle access control solutions. Our approach represents a significant step toward improving security and automation in real-time vehicle management systems.