TY - GEN
T1 - A hierarchical license plate recognition system using supervised K-means and support vector machine
AU - Liu, Wei Chen
AU - Lin, Cheng Hung
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/21
Y1 - 2017/7/21
N2 - In recent years, the use of license plate recognition technology in traffic monitor has attracted a lot of attention because it can be used in a smart city to do criminal investigation and traffic detection. License plate recognition technology has been widely used in parking lot management systems which has fixed shooting angle and lighting environments. The license plate recognition used in traffic monitor will encounter difficulties in character recognition due to factors such as shooting angle, vehicle speed and environment light and shadow. Aiming at the blurred and skewed character images caused by the above factors, this paper presents a hierarchical architecture combining supervised K-means and support vector machine. The supervised K-means is used to classify characters into subgroups. The characters of subgroups can be further classified by support vector machine. The advantage of the proposed approach is to reduce the classes of characters in each subgroup to further reduce the number of SVMs and their complexity, and thus improve the accuracy of character recognition. Experimental results show that our proposed hierarchical architecture achieves an accuracy of 98.89% in character recognition. Compared with the license plate recognition technology using SVM alone, we get a 3.6% improvement in recognition rate.
AB - In recent years, the use of license plate recognition technology in traffic monitor has attracted a lot of attention because it can be used in a smart city to do criminal investigation and traffic detection. License plate recognition technology has been widely used in parking lot management systems which has fixed shooting angle and lighting environments. The license plate recognition used in traffic monitor will encounter difficulties in character recognition due to factors such as shooting angle, vehicle speed and environment light and shadow. Aiming at the blurred and skewed character images caused by the above factors, this paper presents a hierarchical architecture combining supervised K-means and support vector machine. The supervised K-means is used to classify characters into subgroups. The characters of subgroups can be further classified by support vector machine. The advantage of the proposed approach is to reduce the classes of characters in each subgroup to further reduce the number of SVMs and their complexity, and thus improve the accuracy of character recognition. Experimental results show that our proposed hierarchical architecture achieves an accuracy of 98.89% in character recognition. Compared with the license plate recognition technology using SVM alone, we get a 3.6% improvement in recognition rate.
KW - Character recognition
KW - K-means
KW - License plate recognition
KW - Support vector machine
UR - http://www.scopus.com/inward/record.url?scp=85028550769&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85028550769&partnerID=8YFLogxK
U2 - 10.1109/ICASI.2017.7988244
DO - 10.1109/ICASI.2017.7988244
M3 - Conference contribution
AN - SCOPUS:85028550769
T3 - Proceedings of the 2017 IEEE International Conference on Applied System Innovation: Applied System Innovation for Modern Technology, ICASI 2017
SP - 1622
EP - 1625
BT - Proceedings of the 2017 IEEE International Conference on Applied System Innovation
A2 - Meen, Teen-Hang
A2 - Lam, Artde Donald Kin-Tak
A2 - Prior, Stephen D.
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Applied System Innovation, ICASI 2017
Y2 - 13 May 2017 through 17 May 2017
ER -
