Perceptual License Plate Super-Resolution with CTC Loss
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F20%3A00525022" target="_blank" >RIV/67985556:_____/20:00525022 - isvavai.cz</a>
Alternative codes found
RIV/00216305:26230/20:PU147774
Result on the web
<a href="http://dx.doi.org/10.2352/ISSN.2470-1173.2020.6.IRIACV-052" target="_blank" >http://dx.doi.org/10.2352/ISSN.2470-1173.2020.6.IRIACV-052</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.2352/ISSN.2470-1173.2020.6.IRIACV-052" target="_blank" >10.2352/ISSN.2470-1173.2020.6.IRIACV-052</a>
Alternative languages
Result language
angličtina
Original language name
Perceptual License Plate Super-Resolution with CTC Loss
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Electronic Imaging, Intelligent Robotics and Industrial Applications using Computer Vision 2020
ISBN
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ISSN
2470-1173
e-ISSN
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Number of pages
5
Pages from-to
052
Publisher name
Society for Imaging Science and Technology
Place of publication
Springfield
Event location
Burlingame, California
Event date
Jan 26, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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