Vehicle License Number Plate Recognition Using Open CV

Abstract

The rise of automated number plate recognition (ANPR) systems has gained significant attention lately due to their many applications in areas like traffic management, parking management, and law enforcement. This paper introduces a new ANPR method using OpenCV, an open-source computer vision library. Building ANPR systems fits well with OpenCV since it provides a flexible platform for image processing and computer vision tasks. The proposed method detects and recognizes number plates from images and video streams by using OpenCV's features for extraction, image preprocessing, and machine learning. Key steps in the process include character segmentation, optical character recognition (OCR), and license plate localization. These strategies improve the accuracy and effectiveness of the ANPR system. The paper includes extensive experiments using various real-world datasets that feature different lighting conditions, camera angles, and vehicle types to evaluate the system's performance. The results demonstrate the effectiveness of the OpenCV-based ANPR system in recognizing license plates, achieving high accuracy and resilience. The paper also discusses potential uses for the ANPR system in security systems, vehicle tracking, and traffic monitoring. It addresses scalability and real-time implementation to ensure the system works well in different situations.