AUTOMATIC LICENSE PLATE RECOGNITION SYSTEM

A SYSTEMATIC SURVEY

Abstract

The capacity to naturally distinguish and extract License plate data from pictures or video streams has gathered noteworthy consideration in later a long time, owing to its potential to upgrade security, streamline activity operations, and encourage effective information collection. The Vehicle Number Plate Recognition (VNPR) system has a broader variety of applications. A sophisticated License Plate Recognition (LPR) system can be smoothly incorporated into existing processes including law enforcement, monitoring, and toll station services. Existing approaches for License Plate Recognition are limited to datasets like CCPD, AOLP, etc., and operation specific, so many of them require a constrained environment to meet the needs of the intended application. Even if there are many Vehicle Number Plate Recognition systems available today, the task is still difficult because of several aspects such as the fast-moving vehicles, inconsistent vehicle number plates, contrast problems, language of the vehicle number, processing and memory limitations, camera mount position, motion-blur, reflections, tolerance to distortion, and varying lighting conditions. The methodologies and procedures employed for ALPR in Deep Learning, Computer Vision, and Machine Learning domains in contemporary literature are investigated in this study. This paper gives a comparative study of the techniques and algorithms used for various tasks included in Vehicle Number Plate Recognition (VNPR) systems such as License Plate Detection, License Plate Recognition, Character Segmentation, etc. We outline a critical and constructive analysis of relevant studies in the ALPR, and it will also give directions for future research, and optimization of the current approaches.