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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

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • 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

  • ISSN

    2470-1173

  • e-ISSN

  • 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