Detail publikace

Perceptual license plate super-resolution with CTC loss

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"
}