[26] AUTOMATIC VEHICLE NUMBER PLATE DETECTION AND RECOGNITION SYSTEM USING COMPUTER VISION AND DEEP LEARNING

How to Cite the Article: R. Prasad Rao, S. Phani Varaprasad, B. Roopa, A. Ramesh, A. Vamsi Krishna & K. Nukesh (2026). Automatic Vehicle Number Plate Detection and Recognition System using Computer Vision and Deep Learning. International Journal of Multidisciplinary Research & Reviews, 5(5),325-332. https://doi.org/10.56815/ijmrr.v5i5.2026.325-332

Abstract

Automatic Number Plate Recognition (ANPR) systems have become essential components in modern intelligent transportation systems, serving critical applications in traffic management, law enforcement, parking automation, and toll collection. This project presents a comprehensive ANPR system developed using state-of-the-art computer vision and deep learning techniques. The system leverages YOLOv8 (You Only Look Once version 8) for real-time object detection to localize license plates within vehicle images, combined with EasyOCR for accurate optical character recognition. The implementation utilizes Python as the primary programming language, with Streamlit providing an intuitive web-based user interface that supports both static image upload and live camera feed
processing. The proposed system achieves 92% accuracy in detecting and recognizing vehicle number plates under various environmental conditions including different lighting scenarios and angles. The integration of deep learning models enables robust performance compared to traditional image processing methods, with significantly improved detection rates and reduced processing time. The system architecture follows a modular design approach, facilitating easy maintenance and future enhancements.