Deep Learning Model for Automatic Number/License Plate Detection and Recognition System in Campus Gates
Twana Mustafa, Murat Karabatak
Abstract
Automatic Number Plate Recognition (ANPR) is a technique designed to read vehicle number plates without human intervention using high speed image capture with supporting illumination. Automatic Number Plate Recognition (ANPR) is a critical technology that enables the monitoring and control of road traffic and parking management, towing systems, vehicle gate entry management, etc. This paper explores the use of deep learning techniques, including OpenCV, YOLO, PaddleOCR, and Tesseract OCR, in combination with Python programming language, to develop ANPR systems. The study investigates the effectiveness of these techniques in detecting and recognizing vehicle number plates under different conditions, including lightly, sunny, rainy, and darkness environments. Additionally, the paper presents the results of experiments conducted to evaluate the accuracy and effectiveness of the ANPR system. The study finds that the integration of deep learning and OCR techniques provides a robust solution for ANPR under different environmental conditions. The findings of this study have important implications for the development of efficient and accurate ANPR systems in the future.