Vehicle Plate Recognition System Research Articles

A vehicle plate recognition system based on deep learning algorithms

A vehicle plate recognition system based on deep learning algorithms

Vehicle Monitoring System

In the fastmoving world, it is nearly impossible to check and verify the details of each and every vehicle manually. So, in order to untangle this problem, we came up with vehicle monitoring system. The prime idea of our project focusses on drawing out the vehicle registration number from the number plates captured using the camera and process the captured number plate image using various algorithms and eventually store the processed data. This data can be used for monitoring the vehicles in parking lots. Management of cars in parking lots requires a lot of human intervention. Parking lots have been installed with cameras, but none of them serve the purpose. They are just installed for video surveillance and none of them solve the problem. Previously, i.e. in the mini project part, we proposed an Automatic Vehicle Plate Recognition system, which captures the vehicle image, processes the number plate and gives out the details of the vehicle. With the help of this module, we propose a system which can be used in parking lots to monitor the vehicles entering and exiting the lot. We can even run a background check on vehicles using the number plate details and alert the police department in case if the vehicle is suspicious.

Support Vector Machine Method to Reduce the Execution Time of Vehicle Plate Recognition System

This research aims to create a vehicle plate detection and recognition system with Cascade Classifier, Support Vector Machine (SVM) and Optical Character Recognition (OCR). Cascade Classifier with Local Binary Patterns (LBP)descriptor is used todetectthe carlicence plate (Coarse Location). SVM is used to reduce plate candidate detection error andthe execution time. Optical Character Recognition (OCR) is used to recognize characters in plates. The system test is performed using 19 video data of moving vehicles at night and rain conditions. Each video has a duration of 30 seconds and contains 4-10 cars per video. The testing results reduce the execution time of vehicle plate recognition systemreached60% with the average accuracy of plate recognition is 61.94%.