scholarly journals License Plate Character Recognition using Riesz Fractional and Convolutional Neural Network

2019 ◽  
Vol 8 (9S3) ◽  
pp. 1432-1436
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Recognition System ◽  
Traffic Monitoring ◽  
License Plate ◽  
License Plate Recognition ◽  
Character Size ◽  
Parking Management ◽  
Font Type

Automatic license plate recognition system is mostly used for identification of vehicles. This system is used in traffic monitoring, parking management and identification of theft vehicles. As in India the license plate regulations are not strictly followed, it is often difficult to identify the plate with different font type and character size. One more major problem in license plate recognition is low quality of images which affected via severe illumination condition. In this paper, a Riesz fractional mathematical model is proposed for enhancing the edges, which results in improving the performance of text recognition. The text in the license plate is recognized using the convolution neural network and the results showed better accuracy.

scholarly journals License Plate Recognition for Vehicle Surveillance

2020 ◽  
pp. 134-138
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition System ◽  
Traffic Monitoring ◽  
Color Contrast ◽  
License Plate ◽  
License Plate Recognition ◽  
Optical Character

License plate recognition system plays very important role in various security aspects which includes entry monitoring of a particular vehicle in commercial complex, traffic monitoring , identification of threats and many more. In past few years many different methods has been adopted for license plate recognition system but still there is little more chance to work on real time difficulties which come across while license plate recognition like speed of vehicle, angle of license plate in picture, background of picture or color contrast of image, reflection on the license plate and so on. The combination of object detection, image processing, and pattern recognition are used to fulfill this application. In the proposed architecture , system will capture a small video and using Google's OCR(Optical Character Recognition) system will recognize license number, if that number get found in database gate will get open with the help of Arduino Uno.

scholarly journals CHARACTER RECOGNITION FOR INDONESIAN LICENSE PLATE BY USING IMAGE ENHANCEMENT AND CONVOLUTIONAL NEURAL NETWORK

2020 ◽  
Vol 14 (2) ◽  
pp. 145-152
Author(s):  
Sahid Bismantoko ◽  
M. Rosyidi ◽  
Umi Chasanah ◽  
Adityo Suksmono ◽  
Tri Widodo
Keyword(s):  
Neural Network ◽  
Image Enhancement ◽  
Character Recognition ◽  
Traffic Monitoring ◽  
Validation Dataset ◽  
License Plate ◽  
Intelligent Transport System ◽  
Intelligent Transport ◽  
Parking Management ◽  
Toll Collection

Many Intelligent Transport System technology have been applied in real world problems such as traffic monitoring, parking management, toll collection, law enforcement. ALPR system is one of the ITS technologies that is widely applied, however this ALPR system can not produce faultless recognition yet, especially for Indonesia license plate. In this research, image enhancement and Convolution Neural Network are proposed to the character recognition. The dataset used in this research are Indonesia license plate. The first step is train dataset to recognize character and evaluate the model with recall, precision, and f-1 score from test dataset. The model achieves accuracy and loss just over 0.99 and just below 0.01 on validation dataset respectively.Key Words : ALPR; ITS; Recall; Precision; F-1 Score; Accuracy; Loss.

Automatic Car Number Plate Recognition System for Authorization

2017 ◽  
Vol MCSP2017 (01) ◽  
pp. 30-34
Author(s):  
Somalin Sandha ◽  
Debaraj Rana
Keyword(s):  
Neural Network ◽  
Recognition System ◽  
Performance Level ◽  
License Plate ◽  
License Plate Recognition ◽  
Human Power ◽  
Automatic License Plate Recognition

In present day scenario the security and authentication is very much needed to make a safety world. Beside all security one vital issue is recognition of number plate from the car for Authorization. In the busy world everything cannot be monitor by a human, so automatic license plate recognition is one of the best application for authorization without involvement of human power. In the proposed method we have make the problem into three fold, firstly extraction of number plate region, secondly segmentation of character and finally Authorization through recognition and classification. For number plate extraction and segmentation we have used morphological based approaches where as for classification we have used Neural Network as classifier. The proposed method is working well in varieties of scenario and the performance level is quiet good.

Dynamic License Plate Localization for Vehicles on Multi-lane Using Single Camera

2013 ◽  
Vol 300-301 ◽  
pp. 740-745
Author(s):  
Hung Li Tseng ◽  
Chao Nan Hung ◽  
Chiu Ching Tuan ◽  
You Ru Wen ◽  
Wen Tzeng Huang ◽  
...  
Keyword(s):  
High Resolution ◽  
Recognition System ◽  
License Plate ◽  
Single Camera ◽  
License Plate Recognition ◽  
Hardware Cost ◽  
Localization System ◽  
Parking Management ◽  
Toll Collection ◽  
Localization Systems

LPR (License Plate Recognition) System has been widely used in highway toll collection, parking management, various traffic regulations enforcement and other systems. Currently, most of the existing LPL (license plate localization) systems are with single camera that is limited to recognizing vehicles in one lane. In this paper we design a license plate localization system that simultaneously recognizes license plates of vehicles on multi-lane by using single high-resolution camera. Our approach significantly reduces the hardware cost of LPR system without sacrificing the accuracy of recognition. And our success rate is about 94%.

BP-Neural Network for Plate Number Recognition

2016 ◽  
Vol 8 (3) ◽  
pp. 34-45 ◽  
Author(s):  
Jia Wang ◽  
Wei Qi Yan
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Back Propagation ◽  
License Plate ◽  
License Plate Recognition ◽  
Traffic Surveillance ◽  
Plate Number ◽  
Number Recognition ◽  
Traffic Systems ◽  
Artificial Neural Network Ann

The License Plate Recognition (LPR) as one crucial part of intelligent traffic systems has been broadly investigated since the boosting of computer vision techniques. The motivation of this paper is to probe in plate number recognition which is an important part of traffic surveillance events. In this paper, locating the number plate is based on edge detection and recognizing the plate numbers is worked on Back-Propagation (BP) Artificial Neural Network (ANN). Furthermore, the authors introduce the system implementation and take advantage of the well-known Matlab platform to delve how to accurately recognize plate numbers. There are 80 samples adopted to test and verify the proposed plate number recognition method. The experimental results demonstrate that the accuracy of the authors' character recognition is above 70%.

Research of License Plate Recognition System Based on Labview

2015 ◽  
Vol 734 ◽  
pp. 646-649
Author(s):  
Zhong Hua Hu ◽  
Chen Tang
Keyword(s):  
Virtual Instrument ◽  
Character Recognition ◽  
Traffic Management ◽  
Intelligent Transportation System ◽  
Recognition System ◽  
License Plate ◽  
Morphological Operations ◽  
License Plate Recognition ◽  
Plate Location ◽  
Traffic Management System

The vehicle license plate recognition system is the intelligent traffic management system based on the image and the character recognition technology, which is an important part of the intelligent transportation system. This paper introduces a method of vehicle license plate location based on edge detection and morphological operations, virtual instrument is combined with machine vision of the license plate recognition method [1]. Finally the license plate number of the vehicle is get. Experiment results show that such method can simplify the algorithm and has some correct location rate.

Research and Design of License Plate Recognition System

2014 ◽  
Vol 556-562 ◽  
pp. 2623-2627
Author(s):  
Feng Ran ◽  
Fa Yu Zhang ◽  
Mei Hua Xu
Keyword(s):  
Adaptive Learning ◽  
Character Recognition ◽  
Complete System ◽  
Recognition System ◽  
Morphological Processing ◽  
Character Segmentation ◽  
License Plate ◽  
License Plate Recognition ◽  
Research And Design ◽  
Speed And Accuracy

Introduce a complete system of license plate recognition: using morphological processing and priori knowledge of license plate to discern the location of license plate, accomplishing tilt correction through Radon transform, then fulfilling character segmentation of accurate positioning license plate by projection, finishing character recognition through BP neural network which was improved by the use of adaptive learning rate and momentum factor. With the programming and verification on Matlab experimental platform, experimental results show that we can have a preferable recognition speed and accuracy.

scholarly journals Bus Attendance System using Optical Character Recognition

2020 ◽  
Vol 9 (4) ◽  
pp. 2133-2136
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
License Plate ◽  
License Plate Recognition ◽  
Surveillance Camera ◽  
Optical Character ◽  
The Neural Network ◽  
Character Extraction ◽  
Image Dataset

In today’s world managing the records of attendance of staffs, students, employee or bus is a tedious task. This project focuses on automating the bus attendance process through vehicle license plate recognition. As, the license plate is a feature that is peculiar to every vehicle, it would help in efficiently marking the bus attendance. The bus attendance system using RFID is a time consuming process. Hence we developed a project to efficiently mark attendance using number plate recognition and OCR. The system was trained using faster RCNN model with bus image dataset. The proposed system is the number plate is captured through surveillance camera and the captured image will be passed as an input to the neural network for training and the number plate will be detected. Character extraction is done using OCR and extracted character matched will be checked with the database and the attendance for particular bus will be marked.

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