Study On Advanced Vehicle Number Plate Detection Systems-A Review

Abstract

In response to the rising volume of vehicles on roadways, there's an urgent demand for advanced technologies to bolster security and streamline traffic-related operations. This project introduces an innovative solution: an Advanced Automatic Number Plate Detection System. Leveraging Python, Easy OCR, and Open CV, this system aims to develop a sophisticated Automatic Number Plate Recognition (ANPR) mechanism capable of accurately detecting license plates, extracting alphanumeric characters, and providing actionable insights for various applications. Python is chosen for its versatility and ease of use, serving as the primary programming language. OpenCV is employed for its robust image processing tools and license plate detection capabilities, while Easy OCR simplifies character extraction from license plates. Key features include license plate detection through OpenCV algorithms, character recognition via Easy OCR, and real-time processing suitable for tasks like traffic monitoring and surveillance. The project follows a structured approach encompassing data collection, image pre-processing, machine learning model training, integration, and thorough testing. Anticipated outcomes include a fully operational ANPR system with high accuracy in license plate detection and character recognition, surpassing conventional methods. Its potential integration into real-world applications such as traffic management underscores its significance in addressing challenges posed by increasing vehicle numbers, with YOLO v8 utilized for precise outcomes. Keywords- Python, Open CV, YOLO, tensorflow, Machine learning, ANN, CNN, and Easy OCR