TY - GEN
T1 - Multilingual and skew license plate detection based on extremal regions
AU - Qian, Rongqiang
AU - Zhang, Bailing
AU - Coenen, Frans
AU - Yue, Yong
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2018/6/21
Y1 - 2018/6/21
N2 - License Plate Detection (LPD) is an important component of many applications involving security and traffic surveillance. Despite current progress, lots of hurdles remain in the way of a robust LPD system. This is particularly true for the detection of license plates with different layouts and skew angles. In this paper, a novel LPD system is proposed for detecting English and Chinese license plates with large skew angles. The proposed system consists of three main stages: 1) a character proposal module to find candidate characters based on Extremal Regions (ERs); 2) feature extraction and classification relies on Convolutional Neural Network (CNN); 3) license plate detection by region linking. The new method much improves on the robustness of existing approaches by leaving out character segmentation. The performance of the proposed license plate localization algorithm is verified using different datasets of vehicle images, including a large field-captured dataset, a skew and tilt dataset, a 12 countries dataset and two benchmark datasets. For Chinese civilian vehicles, the accuracy of plate localization is over 98.3%.
AB - License Plate Detection (LPD) is an important component of many applications involving security and traffic surveillance. Despite current progress, lots of hurdles remain in the way of a robust LPD system. This is particularly true for the detection of license plates with different layouts and skew angles. In this paper, a novel LPD system is proposed for detecting English and Chinese license plates with large skew angles. The proposed system consists of three main stages: 1) a character proposal module to find candidate characters based on Extremal Regions (ERs); 2) feature extraction and classification relies on Convolutional Neural Network (CNN); 3) license plate detection by region linking. The new method much improves on the robustness of existing approaches by leaving out character segmentation. The performance of the proposed license plate localization algorithm is verified using different datasets of vehicle images, including a large field-captured dataset, a skew and tilt dataset, a 12 countries dataset and two benchmark datasets. For Chinese civilian vehicles, the accuracy of plate localization is over 98.3%.
KW - License plate Detection
KW - convolutional neural network
KW - extremal regions
KW - multilingual license plate
KW - skew license plate
UR - https://www.scopus.com/pages/publications/85050181904
U2 - 10.1109/FSKD.2017.8393386
DO - 10.1109/FSKD.2017.8393386
M3 - Conference Proceeding
AN - SCOPUS:85050181904
T3 - ICNC-FSKD 2017 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery
SP - 850
EP - 855
BT - ICNC-FSKD 2017 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery
A2 - Zhao, Liang
A2 - Wang, Lipo
A2 - Cai, Guoyong
A2 - Li, Kenli
A2 - Liu, Yong
A2 - Xiao, Guoqing
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2017
Y2 - 29 July 2017 through 31 July 2017
ER -