TY - GEN
T1 - An efficient license plate recognition system using convolution neural networks
AU - Lin, Cheng Hung
AU - Lin, Yong Sin
AU - Liu, Wei Chen
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/6/22
Y1 - 2018/6/22
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.
KW - convolution neural networks
KW - license plate recognition system
KW - smart city
UR - http://www.scopus.com/inward/record.url?scp=85050294657&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050294657&partnerID=8YFLogxK
U2 - 10.1109/ICASI.2018.8394573
DO - 10.1109/ICASI.2018.8394573
M3 - Conference contribution
AN - SCOPUS:85050294657
T3 - Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018
SP - 224
EP - 227
BT - Proceedings of 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018
A2 - Lam, Artde Donald Kin-Tak
A2 - Prior, Stephen D.
A2 - Meen, Teen-Hang
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th IEEE International Conference on Applied System Innovation, ICASI 2018
Y2 - 13 April 2018 through 17 April 2018
ER -
