More Web Proxy on the site http://driver.im/

research-article

Deep automatic license plate recognition system

Authors:

Zitha Sasindran,

Anoop Rajagopal,

Harish S Bharadwaj,

K R RamakrishnanAuthors Info & Claims

ICVGIP '16: Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing

Article No.: 6, Pages 1 - 8

https://doi.org/10.1145/3009977.3010052

Published: 18 December 2016 Publication History

Abstract

Automatic License Plate Recognition (ALPR) has important applications in traffic surveillance. It is a challenging problem especially in countries like in India where the license plates have varying sizes, number of lines, fonts etc. The difficulty is all the more accentuated in traffic videos as the cameras are placed high and most plates appear skewed. This work aims to address ALPR using Deep CNN methods for real-time traffic videos. We first extract license plate candidates from each frame using edge information and geometrical properties, ensuring high recall. These proposals are fed to a CNN classifier for License Plate detection obtaining high precision. We then use a CNN classifier trained for individual characters along with a spatial transformer network (STN) for character recognition. Our system is evaluated on several traffic videos with vehicles having different license plate formats in terms of tilt, distances, colors, illumination, character size, thickness etc. Results demonstrate robustness to such variations and impressive performance in both the localization and recognition. We also make available the dataset for further research on this topic.

References

[1]

Automatic license plate recognition library. https://github.com/openalpr/openalpr, 2015.

[2]

Nvidia digits. github.com/NVIDIA/DIGITS, 2016.

[3]

R. Chen and Y. Luo. An improved license plate location method based on edge detection. Physics Procedia, 24:1350--1356, 2012.

[4]

X. He H. Zhang, W. Jia and Q. Wu. Learning-based license plate detection using global and local features. In Internation Conference on Pattern Recognition, volume 2, pages 1102--1105, 2006.

Digital Library

[5]

Q. Ye J. Jiao and Q. Huang. A configurable method for multi-style license plate recognition. Pattern Recognition, 42(3):358--369, 2009.

Digital Library

[6]

H. Li and C. Shen. Reading car license plates using deep convolutional neural networks and lstms. arXiv preprint arXiv:1601.05610, 2016.

[7]

A. Vedaldi M. Jaderberg, K. Simonyan and A. Zisserman. Synthetic data and artificial neural networks for natural scene text recognition. arXiv preprint arXiv:1406.2227, 2014.

[8]

K. Simonyan M. Jaderberg and A. Zisserman. Spatial transformer networks. In Advances in Neural Information Processing Systems, pages 2008--2016, 2015.

Digital Library

[9]

M. Paulin, Z. Harchaoui F. Perronnin C. Revaud, Jérôme, and Schmid. Transformation pursuit for image classification. In IEEE Conference on Computer Vision and Pattern Recognition, pages 3646--3653, 2014.

Digital Library

[10]

M. Shehata S. Du, M. Ibrahim and W. Badawy. Automatic license plate recognition (alpr): A state-of-the-art review. IEEE Transactions on Circuits Systems Video Technology, 23(2):311--325, 2013.

Digital Library

[11]

O. Katai H. Kawakami S. Nomura, K. Yamanaka and T. Shiose. A novel adaptive morphological approach for degraded character image segmentation. Pattern Recognition, 38(11):1961--1975, 2005.

Digital Library

[12]

O. Ishaq S. Rasheed, A. Naeem. Automated number plate recognition using hough lines and template matching. In Proceedings of the World Congress on Engineering. and Computer Science, volume 1, pages 24--26, 2012.

[13]

Theano Development Team. Theano: A Python framework for fast computation of mathematical expressions. arXiv e-prints, abs/1605.02688, May 2016.

[14]

H. Zhang W. Jia and H.Huaifeng. Region-based license plate detection. Journal of Network and Computer Applications, 30(4):1324--1333, 2007.

Digital Library

[15]

J. Donahue Y. Jia, E. Shelhamer et al. Caffe: Convolutional architecture for fast feature embedding. arXiv preprint arXiv:1408.5093, 2014.

[16]

H. Yoon Hosub Y. Yoon, KD Ban and J. Kim. Blob extraction based character segmentation method for automatic license plate recognition system. In IEEE Conference on Systems, Man, and Cybernetics, pages 2192--2196, 2011.

[17]

L. Bai Y. Zhang, Z. Zha. A license plate character segmentation method based on character contour and template matching. In Applied Mechanics and Materials, volume 333, pages 974--979, 2013.

[18]

H. Guo-Jiang Z. Wei-gang and J. Xing. A study of locating vehicle license plate based on color feature and mathematical morphology. In International Conference on Signal Processing, volume 1, pages 748--751, 2002.

[19]

S. Yang Z. Zhao and X. Ma. Chinese license plate recognition using a convolutional neural network. In PACIIA, volume 1, pages 27--30. IEEE, 2008.

Digital Library

Cited By

V NRajan SSriram SM T(2024)Automatic License Plate Detection Using YOLOv92024 International Conference on Cybernation and Computation (CYBERCOM)10.1109/CYBERCOM63683.2024.10803199(236-240)Online publication date: 15-Nov-2024
https://doi.org/10.1109/CYBERCOM63683.2024.10803199
Saravanan MRajini G(2024)Comprehensive study on the development of an automatic helmet violator detection system (AHVDS) using advanced machine learning techniquesComputers and Electrical Engineering10.1016/j.compeleceng.2024.109289118:PAOnline publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1016/j.compeleceng.2024.109289
Irraivan EPhang SGudipalli A(2023)Automatic Number Plate Recognition and QR Code Double Authentication System for a Carpark2023 Innovations in Power and Advanced Computing Technologies (i-PACT)10.1109/i-PACT58649.2023.10434367(1-6)Online publication date: 8-Dec-2023
https://doi.org/10.1109/i-PACT58649.2023.10434367
Show More Cited By

Index Terms

Deep automatic license plate recognition system
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object detection
        Object recognition
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks

Recommendations

Automatic License Plate Recognition via sliding-window darknet-YOLO deep learning
Abstract
Automatic License Plate Recognition (ALPR) is an important research topic in the intelligent transportation system and image recognition fields. In this work, we address the problem of car license plate detection using a You Only Look ...
Automatic license plate recognition

Automatic license plate recognition (LPR) plays an important role in numerous applications and a number of techniques have been proposed. However, most of them worked under restricted conditions, such as fixed illumination, limited vehicle speed, ...
A Chinese license plate recognition system
ASMCSS'09: Proceedings of the 3rd International Conference on Applied Mathematics, Simulation, Modelling, Circuits, Systems and Signals

As one of the hot issues of studying in the field of modern traffic engineering, the license plate recognition (LPR) has attracted more and more attention. In this paper, an experimental system for the LRP of China-style license plates is presented. The ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

ICVGIP '16: Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing

December 2016

743 pages

ISBN:9781450347532

DOI:10.1145/3009977

Program Chairs:
Dhruv Batra
Georgia Tech
,
Michael Brown
York University, Canada
,
Vijay Natarajan
Indian Institute of Science, Bangalore

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Google Inc.
QI: Qualcomm Inc.
Tata Consultancy Services
NVIDIA
MathWorks: The MathWorks, Inc.
Microsoft Research: Microsoft Research

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 December 2016

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

ICVGIP '16

Sponsor:

QI
MathWorks
Microsoft Research

ICVGIP '16: Indian Conference on Computer Vision, Graphics and Image Processing

December 18 - 22, 2016

Assam, Guwahati, India

Acceptance Rates

ICVGIP '16 Paper Acceptance Rate 95 of 286 submissions, 33%;

Overall Acceptance Rate 95 of 286 submissions, 33%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

43
Total Citations
View Citations
1,117
Total Downloads

Downloads (Last 12 months)43
Downloads (Last 6 weeks)8

Reflects downloads up to 22 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

V NRajan SSriram SM T(2024)Automatic License Plate Detection Using YOLOv92024 International Conference on Cybernation and Computation (CYBERCOM)10.1109/CYBERCOM63683.2024.10803199(236-240)Online publication date: 15-Nov-2024
https://doi.org/10.1109/CYBERCOM63683.2024.10803199
Saravanan MRajini G(2024)Comprehensive study on the development of an automatic helmet violator detection system (AHVDS) using advanced machine learning techniquesComputers and Electrical Engineering10.1016/j.compeleceng.2024.109289118:PAOnline publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1016/j.compeleceng.2024.109289
Irraivan EPhang SGudipalli A(2023)Automatic Number Plate Recognition and QR Code Double Authentication System for a Carpark2023 Innovations in Power and Advanced Computing Technologies (i-PACT)10.1109/i-PACT58649.2023.10434367(1-6)Online publication date: 8-Dec-2023
https://doi.org/10.1109/i-PACT58649.2023.10434367
Song BYi YZhou TYang JLiu L(2023)Undermining License Plate Recognition: A Data Poisoning Attack2023 IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)10.1109/TrustCom60117.2023.00032(72-78)Online publication date: 1-Nov-2023
https://doi.org/10.1109/TrustCom60117.2023.00032
Zhou ZLi YLi JYu KKou GWang MGupta B(2023)GAN-Siamese Network for Cross-Domain Vehicle Re-Identification in Intelligent Transport SystemsIEEE Transactions on Network Science and Engineering10.1109/TNSE.2022.319991910:5(2779-2790)Online publication date: 1-Sep-2023
https://doi.org/10.1109/TNSE.2022.3199919
Bhatt RMalik SArora RAgarwal GSharma SDhablia A(2023)Recognition of Criminal Faces From Wild VideosSurveillance System Using VGG-16 Architecture2023 International Conference on Data Science and Network Security (ICDSNS)10.1109/ICDSNS58469.2023.10245450(1-8)Online publication date: 28-Jul-2023
https://doi.org/10.1109/ICDSNS58469.2023.10245450
Sufiun ABijoy MChakraborty NAkash M(2023)Automatic Bengali Number Plate Detection and Authentication using YOLO-V4 and YOLO-V52023 26th International Conference on Computer and Information Technology (ICCIT)10.1109/ICCIT60459.2023.10441416(1-6)Online publication date: 13-Dec-2023
https://doi.org/10.1109/ICCIT60459.2023.10441416
Pang XTian XNie XYin YJiang G(2023)Vehicle re-identification based on grouping aggregation attention and cross-part interactionJournal of Visual Communication and Image Representation10.1016/j.jvcir.2023.10393797(103937)Online publication date: Dec-2023
https://doi.org/10.1016/j.jvcir.2023.103937
Maiano LMontuschi ACaserio MFerri EKieffer FGermanò CBaiocco LRicciardi Celsi LAmerini IAnagnostopoulos A(2023)A deep-learning–based antifraud system for car-insurance claimsExpert Systems with Applications10.1016/j.eswa.2023.120644231(120644)Online publication date: Nov-2023
https://doi.org/10.1016/j.eswa.2023.120644
Thompson DGiscombe AArmstrong KWitter NMurray JWhite DPanther CEdwards-Braham S(2023)An AI-Enabled Vehicle Surveillance System to Tracking Entrance, Exit, and Parking of Vehicles on the University of Technology–Jamaica, Papine CampusThe 6th International Conference on Wireless, Intelligent and Distributed Environment for Communication10.1007/978-3-031-47126-1_1(1-12)Online publication date: 21-Dec-2023
https://doi.org/10.1007/978-3-031-47126-1_1
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents