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Recognition of License Plates Using a Hybrid Statistical Feature Model and Neural Networks  

Lew, Sheen (인천대학교 정보통신공학과)
Jeong, Byeong-Jun ((주)로봇에버 HRI Lab)
Kang, Hyun-Chul (인천대학교 정보통신공학과)
Publication Information
Journal of KIISE:Software and Applications / v.36, no.12, 2009 , pp. 1016-1023 More about this Journal
Abstract
A license plate recognition system consists of image processing in which characters and features are extracted, and pattern recognition in which extracted characters are classified. Feature extraction plays an important role in not only the level of data reduction but also performance of recognition. Thus, in this paper, we focused on the recognition of numeral characters especially on the feature extraction of numeral characters which has much effect in the result of plate recognition. We suggest a hybrid statistical feature model which assures the best dispersion of input data by reassignment of clustering property of input data. And we verify the effectiveness of suggested model using multi-layer perceptron and learning vector quantization neural networks. The results show that the proposed feature extraction method preserves the information of a license plate well and also is robust and effective for even noisy and external environment.
Keywords
license plate; statistical feature; independent component analysis; numeral character recognition; neural network;
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Times Cited By KSCI : 2  (Citation Analysis)
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