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Volume: 32 | Article ID: art00009
Perceptual License Plate Super-Resolution with CTC Loss
  DOI :  10.2352/ISSN.2470-1173.2020.6.IRIACV-052  Published OnlineJanuary 2020

We present a novel method for super-resolution (SR) of license plate images based on an end-to-end convolutional neural networks (CNN) combining generative adversial networks (GANs) and optical character recognition (OCR). License plate SR systems play an important role in number of security applications such as improvement of road safety, traffic monitoring or surveillance. The specific task requires not only realistic-looking reconstructed images but it also needs to preserve the text information. Standard CNN SR and GANs fail to accomplish this requirment. The incorporation of the OCR pipeline into the method also allows training of the network without the need of ground truth high resolution data which enables easy training on real data with all the real image degradations including compression.

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Zuzana Bílková, Michal Hradiš, "Perceptual License Plate Super-Resolution with CTC Lossin Proc. IS&T Int’l. Symp. on Electronic Imaging: Intelligent Robotics and Industrial Applications using Computer Vision,  2020,  pp 52-1 - 52-5,

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