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

Autor: Doo-Hyun Choi, Jong Taek Lee, Kil-Taek Lim, Byung-Gil Han
Rok vydání: 2020
Předmět:
segmentation-free
Computer science
ensemble data
ALPR
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
02 engineering and technology
computer.software_genre
lcsh:Technology
Image (mathematics)
lcsh:Chemistry
End-to-end principle
0202 electrical engineering
electronic engineering
information engineering
General Materials Science
lcsh:QH301-705.5
Instrumentation
License
Fluid Flow and Transfer Processes
lcsh:T
business.industry
Process Chemistry and Technology
Deep learning
General Engineering
020206 networking & telecommunications
Pattern recognition
Optical character recognition
Real image
lcsh:QC1-999
Small set
GAN
Computer Science Applications
lcsh:Biology (General)
lcsh:QD1-999
lcsh:TA1-2040
license plate image generation
end-to-end recognition
020201 artificial intelligence & image processing
Artificial intelligence
lcsh:Engineering (General). Civil engineering (General)
business
computer
lcsh:Physics
Generative grammar
Zdroj: Applied Sciences
Volume 10
Issue 8
Applied Sciences, Vol 10, Iss 2780, p 2780 (2020)
ISSN: 2076-3417
DOI: 10.3390/app10082780
Popis: 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.
Databáze: OpenAIRE
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