Automatic License Plate Recognition System Using YOLOv4

Abstract

In this research paper, we’ll talk about ALPR technology, which has gained popularity recently because of all the many ways it may be used. The fundamental benefit of this technology is that it may be utilized for a variety of purposes, including travel time analysis, intelligent parking, automated toll collection, intelligent transportation systems in smart cities, and traffic management. Automated License Plate Recognition (ALPR) reads the vehicle’s registration number by first using YOLOv4 for object recognition following which we use OpenCV to enlarge the license plate image and identify the character boxes after which we use Tesseract optical character recognition to identify the various characters and form the license plate number. This system uses several image processing methods to recognize automobiles swiftly and automatically in video or picture material. As technology develops quickly with the introduction of machine learning and deep learning, the cost of computing falls, and the accuracy of used image processing methods rises, the usage of ALPR systems is becoming more widespread. In today’s congested traffic system, a license plate detection system is crucial. It aids in monitoring compliance with traffic laws and other law enforcement operations. There are many instances of reckless driving in India when vehicles break several traffic laws. As a result, a license plate detection system has been suggested and put into use throughout the years to assist with quick and simple traffic law enforcement by automobiles. This work offers a powerful method for character localization, segmentation, and identification inside the located plate. We are going to utilize tesseract OCR and the YoLo V4 approach to solve the License plate recognition system issue and deliver our suggested system with high accuracy