Deep Learning Approaches In Automatic Number Plate Recognition: Result and Analysis

Systems known as Automatic Number Plate Recognition (ANPR), license plate recognition or LPR are now widely used in many sectors such as law enforcement, traffic control, vehicle access etc. This is a technology that uses object detection and optical character recognition to identify the numbers on number plates from input images or video. These systems use high-level hardware and software components together to automatically capture, recognize and process license plate information. Key elements of any ANPR system include high-resolution cameras, image processing algorithms, and optical character recognition (OCR) methods. The operational workflow of an ANPR system comprises several steps like image acquisition, preprocessing, license plate localization, character segmentation, OCR, and post-processing stages. Different applications of ANPR technology include traffic flow monitoring; helping police track stolen vehicles; congestion detection for traffic management purposes; and automated entry/exit processes based on authorized number plate recognition among others for supporting vehicle access control. A accuracy of 90% was achieved by proposed, ANPR systems represent a significant technological advance that improves accuracy; efficiency as well as automation in various critical areas of our lives.