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2018 24th International Conference on Pattern Recognition (ICPR) (2018)
Beijing, China
Aug. 20, 2018 to Aug. 24, 2018
ISSN: 1051-4651
ISBN: 978-1-5386-3789-0
pp: 3698-3703
Yu Cao , Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing, 100876, China
Huiyuan Fu , Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing, 100876, China
Huadong Ma , Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing, 100876, China
ABSTRACT
Currently, license plate recognition plays an important role in numerous applications and a number of technologies have been proposed. However, most of them can only work with single-line license plates. In the practical application scenarios, there are also existing many multi-line license plates. The traditional approaches need to segment the original input images for double-line license plates. This is a very difficult problem in the complex scenes. In order to solve this problem, we propose an end-to-end neural network for both single-line and double-line license plate recognition. It is segmentation-free for the original input license plate images. We view each of these whole images as a unit on feature maps after deep convolution neural network directly. A large number of experiments show that our method is effective. It is better than the state-of-the-art algorithms in SYSU-ITS license plate library data.
INDEX TERMS
Licenses, Feature extraction, Convolution, Character recognition, Task analysis, Convolutional neural networks
CITATION

Y. Cao, H. Fu and H. Ma, "An End-to-End Neural Network for Multi-line License Plate Recognition," 2018 24th International Conference on Pattern Recognition (ICPR), Beijing, China, 2018, pp. 3698-3703.
doi:10.1109/ICPR.2018.8546200
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