SMART LICENSE PLATE RECOGNITION SYSTEM BASED ON IMAGE PROCESSING USING NEURAL NETWORK

V Koval,G Markowsky,V Kochan,V Turchenko,A Sachenko

doi:10.47839/ijc.2.2.203

Abstract

This paper describes the Smart Vehicle Screening System, which can be installed into a tollbooth for automated recognition of vehicle license plate information using a photograph of a vehicle. An automated system could then be implemented to control the payment of fees, parking areas, highways, bridges or tunnels, etc. There are considered an approach to identify vehicle through recognizing of it license plate using image fusion, neural networks and threshold techniques as well as some experimental results to recognize the license plate successfully.

Highlights

The task of vehicle identification can be solved by vehicle license plate recognition
Gradient descent backpropagation method with momentum and adaptive learning rate is used for neural network training [14]
The training vector of neural network consists of 663 elements and it is created by vectors of image rows of the license plate characters in binary format

Summary

INTRODUCTION

The task of vehicle identification can be solved by vehicle license plate recognition. A number of commercial software is developed in this area They cannot be readily used when vehicle image is provided in different styles and formats [1,2,3]. Proposed approach allows removing this drawback by ensemble of two methods: (i) detection and extraction of image region included license plate from source images flow and (ii) recognition of character presented on the license plate. Image processing techniques such as edge detection, thresholding and resampling have been used to locate and isolate the license plate and the characters. The algorithm of license plate recognition (LPR) consists of the following steps: (i) to capture the car's images, (ii) to deblur of image frames, (iii) to extract image of license plate, (iv) to extract characters from license plate image, (v) to recognize license plate characters and identify the vehicle

DEBLURRING TECHNOLOGY AND IMAGE FUSION

LICENSE PLATE IDENTIFICATION

LICENSE PLATE CHARACTERS EXTRACTING

RECOGNIZING OF CHARACTERS USING NEURAL NETWORKS

EXPERIMENTAL RESULTS

CONCLUSIONS