scholarly journals Vehicle License Plate Detection for Parking Offenders Using Automatic License-Plate Recognition

2020 ◽  
Vol 55 (4) ◽  
Author(s):  
Chuan Pratama ◽  
Suci Aulia ◽  
Dadan Nur Ramadan ◽  
Sugondo Hadiyoso
Keyword(s):  
Detection Methods ◽  
Prototype System ◽  
License Plate ◽  
License Plate Recognition ◽  
Test Results ◽  
Accuracy Rate ◽  
Road Space ◽  
Camera Position ◽  
License Plate Detection ◽  
Automatic License Plate Recognition

Vehicles parked illegally on the highway can limit road space and result in congestion. Thus, illegal parking must be monitored and controlled. In this study, a prototype system for detecting the license plates of parking offenders based on image processing was implemented. The first stage in this system is detecting the license plate, then segmenting each character into a separate image. The next stage is converting the character from image to text format, referred to as automatic license-plate recognition. The goal is to send that detected plate license to the database of the authorities, so that the authorities can discover the identity of the parking offender to impose sanctions. In this study, several conditions of acquisition and variations of edge detection methods were tested. Based on the test results, an accuracy rate of 100% was obtained for license plate detection using the Canny method during the morning, with the camera position at 3 meters high, 2 meters of distance, and a 60o angle.

IMAGE PROCESSING OF ANDROID-BASED PATROL ROBOT FEATURING AUTOMATIC LICENSE PLATE RECOGNITION

2015 ◽  
Vol 3 (3) ◽  
pp. 296-301
Author(s):  
S. Madhan ◽  
M. Pradeep
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition Rate ◽  
License Plate ◽  
License Plate Recognition ◽  
Vertical Edge ◽  
Stain Removal ◽  
License Plate Detection ◽  
Novel Methods ◽  
Automatic License Plate Recognition

This work develops an Android-based robot featuring automatic license plate recognition and automatic license plate patrolling. The automatic license plate recognition feature combines 4 self-developed novel methods, Wiener deconvolution vertical edge enhancement, AdaBoost plus vertical-edge license plate detection, vertical edge projection histogram segmentation stain removal, and customized optical character recognition. Besides, the automatic license plate patrolling feature also integrates 3 novel methods, HL2-band rough license plate detection, orientated license plate approaching, and Ad-Hoc-based remote motion control. Implementation results show the license plate detection rate and recognition rate of the Android-based robot are over 99% and over 98%, respectively, under various scene conditions. Especially, the execution time of license plate recognition, including license plate detection, is only about 0.7 second per frame on the Android-based robot.

scholarly journals Recognition of Hybrid Graphic-Text License Plates

2021 ◽  
Vol 25 (4) ◽  
pp. 416-422
Author(s):  
John Anthony C. Jose ◽  
◽  
Allysa Kate M. Brillantes ◽  
Elmer P. Dadios ◽  
Edwin Sybingco ◽  
...  
Keyword(s):  
Temporal Information ◽  
License Plate ◽  
Motion Information ◽  
License Plate Recognition ◽  
Still Images ◽  
License Plate Detection ◽  
Tracking And Recognition ◽  
Automatic License Plate Recognition

Most automatic license-plate recognition (ALPR) systems use still images and ignore the temporal information in videos. Videos provide rich temporal and motion information that should be considered during training and testing. This study focuses on creating an ALPR system that uses videos. The proposed system is comprised of detection, tracking, and recognition modules. The system achieved accuracies of 81.473% and 84.237% for license-plate detection and classification, respectively.

scholarly journals Automatic License Plate Recognition: An Efficient and Layout-Independent System Based on the YOLO Detector

2020 ◽  
Author(s):  
Rayson Laroca ◽  
David Menotti
Keyword(s):  
Law Enforcement ◽  
License Plate ◽  
License Plate Recognition ◽  
Unified Approach ◽  
Practical Applications ◽  
Major Shortcoming ◽  
License Plate Detection ◽  
Traffic Law Enforcement ◽  
Public Datasets ◽  
Automatic License Plate Recognition

Automatic License Plate Recognition (ALPR) has been a frequent topic of research due to many practical applications, such as border control and traffic law enforcement. This work presents an efficient, robust and layout-independent ALPR system based on the YOLO object detector that contains a unified approach for license plate detection and layout classification and that leverages post-processing rules in the recognition stage to eliminate a major shortcoming of existing ALPR systems (being layout dependent). We also introduce a publicly available dataset for ALPR, called UFPR-ALPR, that has become very popular, having been downloaded more than 650 times by researchers from 80 different countries over the past two years. The proposed system, which performs in real time even when there are 4 vehicles in the scene, outperformed both previous works and commercial systems on four public datasets widely used in the literature. The entire ALPR system (i.e., the architectures and weights), along with all annotations made by us are publicly available at https://web.inf.ufpr.br/vri/publications/layout-independent-alpr/.

scholarly journals Automatic License Plate Recognition: An Efficient and Layout-Independent Approach Based on the YOLO Detector

2020 ◽  
Author(s):  
Rayson Laroca ◽  
David Menotti
Keyword(s):  
Law Enforcement ◽  
License Plate ◽  
License Plate Recognition ◽  
Unified Approach ◽  
Practical Applications ◽  
Major Shortcoming ◽  
License Plate Detection ◽  
Traffic Law Enforcement ◽  
Public Datasets ◽  
Automatic License Plate Recognition

Automatic License Plate Recognition (ALPR) has been a frequent topic of research due to many practical applications, such as border control and traffic law enforcement. This work presents an efficient, robust and layout-independent ALPR system based on the YOLO object detector that contains a unified approach for license plate detection and layout classification and leverages post-processing rules in the recognition stage to eliminate a major shortcoming of existing ALPR systems (being layout dependent). We also introduce a publicly available dataset for ALPR that has become very popular, having been downloaded more than 550 times by researchers from 76 different countries in the last year alone. The proposed system, which performs in real time even when there are 4 vehicles in the scene, outperformed both previous works and commercial systems on four public datasets widely used in the literature.

scholarly journals PVBR-Recog: A YOLOv3-based Brazilian Automatic License Plate Recognition Tool

2019 ◽  
Author(s):  
Pedro Ferreira Alves Pinto ◽  
Antonio José G. Busson ◽  
João P. Forny de Melo ◽  
Sérgio Colcher ◽  
Ruy Luiz Milidiú
Keyword(s):  
Deep Learning ◽  
License Plate ◽  
License Plate Recognition ◽  
Practical Applications ◽  
License Plate Detection ◽  
Detection And Recognition ◽  
Automatic License Plate Recognition

Vehicle’s license plate detection and recognition is a task with several practical applications. It can be applied, for example, in the security segment, identifying stolen cars and controlling cars entry/exit in private areas. This work presents a Deep Learning based tool that uses the cascaded YOLOv3 to simultaneously detect and recognize vehicle plate. In experiments performed, our tool got a recall of 95% in plate detection and 96.2% accuracy in the recognition of the 7 characters of the license plate.

Detection optimization of license plate targets based on AdvancedEAST

2021 ◽  
pp. 1-9
Author(s):  
Feifei Yin ◽  
Jingxuan Wang ◽  
Wei Xiong ◽  
Juanjuan Gao ◽  
Yu Gong
Keyword(s):  
Transfer Learning ◽  
Traffic Management ◽  
Recognition Rate ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Natural Scenes ◽  
License Plate ◽  
License Plate Recognition ◽  
Detection Model ◽  
License Plate Detection

As an important core in the intelligent traffic management system, the technology and application of license plate recognition have become research focus. Detecting the accurate location of a license plate from a vehicle image is considered to be the most crucial step of license plate recognition, which greatly affects the recognition rate and speed of the whole system. Nevertheless, due to the low accuracy of license plate detection in natural scenes, further investigations are still needed in this field in order to make the detection process very efficient. In this paper, We have studied and implemented a convolutional neural network license plate detection algorithm based on transfer learning. According to the invention, new energy license plates and ordinary license plates are adopted as the research objects. The text detection model AdvancedEAST is trained on the license plate images through the transfer learning method, and experiments are carried out on the self-built license plate dataset. The experimental results show that the algorithm can better adapt to light complexity, low resolution, target interference, license plate tilt and other complex conditions. The license plate positioning algorithm has high accuracy in natural scenes, and it is superior to the traditional license plate detection methods.

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.

scholarly journals Automatic License Plate Recognition: A Review with Indonesian Case Study

2018 ◽  
Vol 5 (2) ◽  
pp. 258-270
Author(s):  
Aris Budianto
Keyword(s):  
State Of The Art ◽  
Developed Countries ◽  
Transportation Systems ◽  
Future Research ◽  
License Plate ◽  
License Plate Recognition ◽  
Previous Knowledge ◽  
Video Recognition ◽  
Automatic License Plate Recognition

The Automatic License Plate Recognition (ALPR) has been becoming a new trend in transportation systems automation. The extraction of vehicle’s license plate can be done without human intervention. Despite such technology has been widely adopted in developed countries, developing countries remain a far-cry from implementing the sophisticated image and video recognition for some reasons. This paper discusses the challenges and possibilities of implementing Automatic License Plate Recognition within Indonesia’s circumstances. Previous knowledge suggested in the literature, and state of the art of the automatic recognition technology is amassed for consideration in future research and practice.

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