Monitoring and Detection of Tampered Number Plate Recognition

Abstract: The growing incidents of tampered and damaged vehicle number plates present significant challenges to law enforcement and intelligent transportation systems, as these alterations allow offenders to evade identification and accountability.This project, Detection and Monitoring Defective Number Plate System, aims to enhance traditional ANPR systems by incorporating automated detection and classification of tampered and damaged plates, offering a more comprehensive solution for traffic law enforcement and public safety. The system utilizes a combination of deep learning models, including YOLOv3 for real-time number plate detection and a Convolutional Neural Network (CNN) for classifying plates as tampered, damaged, or intact.