Adaptive local binarization method for recognition of vehicle license plates

  • Authors:

  • Byeong Rae Lee;Kyungsoo Park;Hyunchul Kang;Haksoo Kim;Chungkyue Kim

  • Affiliations:

  • Dept. of Computer Science, Korea National Open University, Seoul, Korea;Dept. of Information and Telecommunication Engineering, University of Incheon, Incheon, Korea;Dept. of Information and Telecommunication Engineering, University of Incheon, Incheon, Korea;Dept. of Information and Telecommunication Engineering, Sungkonghoe University, Seoul, Korea;Dept. of Computer Science and Engineering, University of Incheon, Incheon, Korea

  • Venue:
  • IWCIA'04 Proceedings of the 10th international conference on Combinatorial Image Analysis
  • Year:
  • 2004

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Abstract

A vehicle license-plate recognition system is commonly composed of three essential parts: detecting license-plate region in the acquired images, extracting individual characters, and recognizing the extracted characters. But in the process, the problems like damage of license-plate and unequal light effect make it difficult to detect accurate vehicle license-plate region and to extract letters in that region. In this paper, to extract characters accurately in the license- plate region, a local adaptive binarization method which is robust under non-uniform lighting environment is proposed. To get better binary images, region- based threshold correction based on a prior knowledge of character arrangement in the license-plate is applied. With the proposed binarization method, 96% of 650 sample vehicle license-plates images are correctly recognized. Compared to existing local threshold selection methods, about 5% of improvement in recognition rate is obtained with the same recognition module based on LVQ.