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%.
|Title of host publication||Neural Networks for Signal Processing X, 2000|
|Subtitle of host publication||Proc. IEEE International Workshop on Neural Networks for Signal Processing, 2000. Volume 2|
|Editors||B. Widrow, J. Larsen, L. Guan, E. Wilson, K. Paliwa, S. Douglas, T. Adali|
|Number of pages||10|
|Publication status||Published - 31 Jan 2001|
Kim, K. K., Kim, J. B., Kim, K. I., & Kim, H. J. (2001). Learning-based approach for license plate recognition. In B. Widrow, J. Larsen, L. Guan, E. Wilson, K. Paliwa, S. Douglas, & T. Adali (Eds.), Neural Networks for Signal Processing X, 2000: Proc. IEEE International Workshop on Neural Networks for Signal Processing, 2000. Volume 2 (pp. 614-623). IEEE. https://doi.org/10.1109/NNSP.2000.890140