CNN based Automated Vehicle Registration Number Plate Recognition System

The objective of VRNPR is to extract vehicle license plate information from number plate of vehicles. As the traffic control and vehicle proprietor recognizable proof is a significant issue in every country, it is important to develop such a device that automatically detects those vehicle owners who violates traffic rules and drives fast [1]. There are many VRNPR systems are present but there is challenging factor like accuracy of extraction, speed of vehicles, lightening condition, quality of images [2]. In this paper, different methods of VRNPR and emerging technologies are used to get accurate result. The important work is the detection and recognition of the number plate which is accomplished by the Convolution Neural Network (CNN). Reason to choose CNN is the high accuracy of around 90% even with relatively small training size [4]. We categorize many VRNPR techniques as per their features they used in each stage and compare them in terms of their advantages and disadvantages, accuracy and processing speed.