Learning-based approach for license plate recognition

Kap Kee Kim, Jong Bae Kim, Kwang In Kim, Hang Joon Kim

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

169 Citations (SciVal)


Presents a learning-based approach for the construction of a license-plate recognition system. The system consists of three modules. They are, respectively, the car detection module, the license-plate segmentation module and the recognition module. The car detection module detects a car in a given image sequence obtained from a camera with a simple color-based approach. The segmentation module extracts the license plate in the detected car image using neural networks as filters for analyzing the color and texture properties of the license plate. The recognition module then reads the characters on the detected license plate with a support vector machine (SVM)-based character recognizer. The system has been tested with 1000 video sequences obtained from toll-gates, parking lots, etc., and has shown the following performances on average: car detection rate 100%, segmentation rate 97.5%, and character recognition rate about 97.2%.
Original languageEnglish
Title of host publicationNeural Networks for Signal Processing X, 2000
Subtitle of host publicationProc. IEEE International Workshop on Neural Networks for Signal Processing, 2000. Volume 2
EditorsB. Widrow, J. Larsen, L. Guan, E. Wilson, K. Paliwa, S. Douglas, T. Adali
Number of pages10
ISBN (Print)9780780362789
Publication statusPublished - 31 Jan 2001


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