Vehicle Identification by OCR, RFID and Bluetooth for Toll Roads

Abstract

Automatic payment in toll roads can improve the efficiency of the roads; however, errors in this payment can reduce the satisfaction of both the road users and the operators who will both have to waste time in order to sort out these mistakes. It is suggested to greatly minimize these mistakes by backup the recognition course of action of the license plates by using three different methods: RFID, Bluetooth and OCR in order to make recognition errors as seldom as possible.

Vehicle Security: A Survey of Security Issues and Vulnerabilities, Malware Attacks and Defenses

“…Another way of hackers to bring cash is automated toll booth payments, it may create more points of entry for hackers to steal individual information, for example, visa or banking data. Hackers are hoping to put forth the greatest benefit for the base attempt since the intelligent vehicles are going to have a lot of payment systems in order to provide the comfort for the driver to pay via his vehicle when he goes to toll roads and parking lots [84]. • Infringement on the Driver's Privacy (M2): An attacker could infringe the privacy of drivers by infusing spyware into a vehicle.…”

Invoice Recognition System based on Neural Network

“…In this process, the correct rate of the staff's input information cannot be effectively guaranteed. When the amount of bills is too large, the staffs are prone to errors in the input information when they work for a long time due to the high working pressure [5]. In order to ensure the accuracy of effective information of bills, financial departments of enterprises often need to proofread the information entered by staff repeatedly, which makes the review period of bill reimbursement longer and the reimbursement efficiency lower [6].…”

Deep Learning in Intelligent Navigation

“…The purpose of mapping is twofold: first, to enable the robot to complete navigation work in the recorded area; second, to provide localization assistance by correcting the map through loop feedback to reduce the positioning error of the robot. Specifically, SLAM is a method by which the robot can estimate the current coordinates while drawing the surrounding environment by collecting sensor information such as liDAR, millimeter-wave radar and camera [5]. Real-time positioning and map creation are important requirements for autonomous movement of robots and also the basis of robot intelligence.…”