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Detail publikace
BÍLKOVÁ, Z. HRADIŠ, M.
Originální název
Perceptual license plate super-resolution with CTC loss
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
Originální abstrakt
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 networksn(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.
Klíčová slova
superresolution, license plate recognition, GAN, deblurring
Autoři
BÍLKOVÁ, Z.; HRADIŠ, M.
Vydáno
15. 1. 2020
Nakladatel
Society for Imaging Science and Technology
Místo
Springfield, USA
ISSN
2470-1173
Ročník
2020
Číslo
6
Strany od
52
Strany do
57
Strany počet
5
BibTex
@inproceedings{BUT182964, author="Zuzana {Bílková} and Michal {Hradiš}", title="Perceptual license plate super-resolution with CTC loss", booktitle="IS and T International Symposium on Electronic Imaging Science and Technology", year="2020", volume="2020", number="6", pages="52--57", publisher="Society for Imaging Science and Technology", address="Springfield, USA", doi="10.2352/ISSN.2470-1173.2020.6.IRIACV-052", issn="2470-1173" }