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Open AccessArticle

License Plate Image Generation using Generative Adversarial Networks for End-To-End License Plate Character Recognition from a Small Set of Real Images

1
Electronics and Telecommunications Research Institute, Daegu 42994, Korea
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School of Electronics Engineering, Kyungpook National University, Daegu 41566, Korea
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(8), 2780; https://doi.org/10.3390/app10082780
Received: 2 March 2020 / Revised: 9 April 2020 / Accepted: 11 April 2020 / Published: 16 April 2020
(This article belongs to the Section Computing and Artificial Intelligence)
License Plate Character Recognition (LPCR) is a technology for reading vehicle registration plates using optical character recognition from images and videos, and it has a long history due to its usefulness. While LPCR has been significantly improved with the advance of deep learning, training deep networks for LPCR module requires a large number of license plate (LP) images and their annotations. Unlike other public datasets of vehicle information, each LP has a unique combination of characters and numbers depending on the country or the region. Therefore, collecting a sufficient number of LP images is extremely difficult for normal research. In this paper, we propose LP-GAN, an LP image generation method, by applying an ensemble of generative adversarial networks (GAN), and we also propose a modified lightweight YOLOv2 model for an efficient end-to-end LPCR module. With only 159 real LP images available online, thousands of synthetic LP images were generated by using LP-GAN. The generated images not only looked similar to real ones, but they were also shown to be effective for training the LPCR module. As a result of performance tests with 22,117 real LP images, the LPCR module trained with only the generated synthetic dataset achieved 98.72% overall accuracy, which is comparable to that of training with a real LP image dataset. In addition, we improved the processing speed of LPCR about 1.7 times faster than that of the original YOLOv2 model by using the proposed lightweight model. View Full-Text
Keywords: license plate image generation; ensemble data; segmentation-free; end-to-end recognition; GAN; ALPR license plate image generation; ensemble data; segmentation-free; end-to-end recognition; GAN; ALPR
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Han, B.-G.; Lee, J.T.; Lim, K.-T.; Choi, D.-H. License Plate Image Generation using Generative Adversarial Networks for End-To-End License Plate Character Recognition from a Small Set of Real Images. Appl. Sci. 2020, 10, 2780.

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