An efficient license plate recognition system using convolution neural networks

Cheng Hung Lin; Yong Sin Lin; Wei Chen Liu

doi:10.1109/ICASI.2018.8394573

An efficient license plate recognition system using convolution neural networks

Cheng Hung Lin
, Yong Sin Lin
, Wei Chen Liu

Department of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

69 Citations (Scopus)

Abstract

In recent years, license plate recognition system has become a crucial role in the development of smart cities for vehicle management, investigation of stolen vehicles, and traffic monitoring and control. License plate recognition system has three stages, including license plate localization, character segmentation, and character recognition. Although the license plate recognition system has been successfully applied to the environment-controlled smart parking system, it still faces many challenging in the surveillance system such as congested traffic with multiple plates, ambiguous signs and advertisements, tilting plates, as well as obscure images taken in bad weather and nighttime. In this paper, we propose an efficient license plate recognition system that first detects vehicles and then retrieves license plates from vehicles to reduce false positives on plate detection. Then, we apply convolution neural networks to improve the character recognition of blurred and obscure images. The experimental results show the superiority of the performance in both accuracy and performance in comparison with traditional license plate recognition systems.

Original language	English
Title of host publication	Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018
Editors	Artde Donald Kin-Tak Lam, Stephen D. Prior, Teen-Hang Meen
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	224-227
Number of pages	4
ISBN (Electronic)	9781538643426
DOIs	https://doi.org/10.1109/ICASI.2018.8394573
Publication status	Published - 2018 Jun 22
Event	4th IEEE International Conference on Applied System Innovation, ICASI 2018 - Chiba, Japan Duration: 2018 Apr 13 → 2018 Apr 17

Publication series

Name	Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018

Conference

Conference	4th IEEE International Conference on Applied System Innovation, ICASI 2018
Country/Territory	Japan
City	Chiba
Period	2018/04/13 → 2018/04/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

Keywords

convolution neural networks
license plate recognition system
smart city

ASJC Scopus subject areas

Computer Networks and Communications
Hardware and Architecture
Energy Engineering and Power Technology
Control and Systems Engineering
Mechanical Engineering
Control and Optimization
Modelling and Simulation
Biomedical Engineering

Access to Document

10.1109/ICASI.2018.8394573

Cite this

Lin, C. H., Lin, Y. S., & Liu, W. C. (2018). An efficient license plate recognition system using convolution neural networks. In A. D. K.-T. Lam, S. D. Prior, & T.-H. Meen (Eds.), Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018 (pp. 224-227). (Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASI.2018.8394573

An efficient license plate recognition system using convolution neural networks. / Lin, Cheng Hung; Lin, Yong Sin; Liu, Wei Chen.
Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018. ed. / Artde Donald Kin-Tak Lam; Stephen D. Prior; Teen-Hang Meen. Institute of Electrical and Electronics Engineers Inc., 2018. p. 224-227 (Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Lin, CH, Lin, YS & Liu, WC 2018, An efficient license plate recognition system using convolution neural networks. in ADK-T Lam, SD Prior & T-H Meen (eds), Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018. Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018, Institute of Electrical and Electronics Engineers Inc., pp. 224-227, 4th IEEE International Conference on Applied System Innovation, ICASI 2018, Chiba, Japan, 2018/04/13. https://doi.org/10.1109/ICASI.2018.8394573

Lin CH, Lin YS, Liu WC. An efficient license plate recognition system using convolution neural networks. In Lam ADKT, Prior SD, Meen TH, editors, Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 224-227. (Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018). doi: 10.1109/ICASI.2018.8394573

Lin, Cheng Hung ; Lin, Yong Sin ; Liu, Wei Chen. / An efficient license plate recognition system using convolution neural networks. Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018. editor / Artde Donald Kin-Tak Lam ; Stephen D. Prior ; Teen-Hang Meen. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 224-227 (Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018).

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title = "An efficient license plate recognition system using convolution neural networks",

abstract = "In recent years, license plate recognition system has become a crucial role in the development of smart cities for vehicle management, investigation of stolen vehicles, and traffic monitoring and control. License plate recognition system has three stages, including license plate localization, character segmentation, and character recognition. Although the license plate recognition system has been successfully applied to the environment-controlled smart parking system, it still faces many challenging in the surveillance system such as congested traffic with multiple plates, ambiguous signs and advertisements, tilting plates, as well as obscure images taken in bad weather and nighttime. In this paper, we propose an efficient license plate recognition system that first detects vehicles and then retrieves license plates from vehicles to reduce false positives on plate detection. Then, we apply convolution neural networks to improve the character recognition of blurred and obscure images. The experimental results show the superiority of the performance in both accuracy and performance in comparison with traditional license plate recognition systems.",

keywords = "convolution neural networks, license plate recognition system, smart city",

author = "Lin, \{Cheng Hung\} and Lin, \{Yong Sin\} and Liu, \{Wei Chen\}",

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N2 - In recent years, license plate recognition system has become a crucial role in the development of smart cities for vehicle management, investigation of stolen vehicles, and traffic monitoring and control. License plate recognition system has three stages, including license plate localization, character segmentation, and character recognition. Although the license plate recognition system has been successfully applied to the environment-controlled smart parking system, it still faces many challenging in the surveillance system such as congested traffic with multiple plates, ambiguous signs and advertisements, tilting plates, as well as obscure images taken in bad weather and nighttime. In this paper, we propose an efficient license plate recognition system that first detects vehicles and then retrieves license plates from vehicles to reduce false positives on plate detection. Then, we apply convolution neural networks to improve the character recognition of blurred and obscure images. The experimental results show the superiority of the performance in both accuracy and performance in comparison with traditional license plate recognition systems.

AB - In recent years, license plate recognition system has become a crucial role in the development of smart cities for vehicle management, investigation of stolen vehicles, and traffic monitoring and control. License plate recognition system has three stages, including license plate localization, character segmentation, and character recognition. Although the license plate recognition system has been successfully applied to the environment-controlled smart parking system, it still faces many challenging in the surveillance system such as congested traffic with multiple plates, ambiguous signs and advertisements, tilting plates, as well as obscure images taken in bad weather and nighttime. In this paper, we propose an efficient license plate recognition system that first detects vehicles and then retrieves license plates from vehicles to reduce false positives on plate detection. Then, we apply convolution neural networks to improve the character recognition of blurred and obscure images. The experimental results show the superiority of the performance in both accuracy and performance in comparison with traditional license plate recognition systems.

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An efficient license plate recognition system using convolution neural networks

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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