Recognition of Car License Plates Using Morphological Features, Color Information and an Enhanced FCM Algorithm

  • Authors:

  • Kwang-Baek Kim;Choong-Shik Park;Young Woon Woo

  • Affiliations:

  • Dept. of Computer Engineering, Silla University, Busan, Korea;Dept. of Computer Engineering, Youngdong University, Chungcheongbuk-Do, Korea;Dept. of Multimedia Engineering, Dong-Eui University, Busan, Korea

  • Venue:
  • ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
  • Year:
  • 2007

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Abstract

In modern days, it is very hard to regulate cars of traffic lights violation and speed violation as well as parking violation and management of cars in parking places because of rapid increase of cars. In this paper, we proposed an intelligent recognition system of car license plates to mitigate these problems. The processing sequence of the proposed algorithm is as follows. At first, a license plate segment is extracted from an acquired car image using morphological features and color information, and noises are eliminated from the extracted license plate segment using a line scan algorithm and a grass fire algorithm, and then individual codes are extracted from the license plate segment using 4-directional edge tracking algorithm. Finally the extracted individual codes are recognized by an enhanced FCM algorithm. The enhanced FCM algorithm is a clustering algorithm improved from conventional clustering algorithms having problems that undesirable clustering results to be acquired because of distribution of patterns in cluster spaces. In order to evaluate performance of segment extraction and code recognition of the proposed method, we used 150 car images for experiment. In the results, we could verify the proposed method is more efficient and recognition performance is improved in comparison with conventional car license plate recognition methods.