Detail publikace

TG2: text-guided transformer GAN for restoring document readability and perceived quality

KODYM, O. HRADIŠ, M.

Originální název

TG2: text-guided transformer GAN for restoring document readability and perceived quality

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

Most image enhancement methods focused on restoration of digitized textual documents are limited to cases where the text information is still preserved in the input image, which may often not be the case. In this work, we propose a novel generative document restoration method which allows conditioning the restoration on a guiding signal in form of target text transcription and which does not need paired high- and low-quality images for training. We introduce a neural network architecture with an implicit text-to-image alignment module. We demonstrate good results on inpainting, debinarization and deblurring tasks, and we show that the trained models can be used to manually alter text in document images.A user study shows that that human observers confuse the outputs of the proposed enhancement method with reference high-quality images in as many as 30% of cases.

Klíčová slova

Generative adversarial networks, Attention neural networks, Textual document restoration, Text inpainting

Autoři

KODYM, O.; HRADIŠ, M.

Vydáno

22. 9. 2021

Nakladatel

Springer Verlag

ISSN

1433-2825

Periodikum

International Journal on Document Analysis and Recognition

Ročník

2021

Číslo

1

Stát

Spolková republika Německo

Strany od

1

Strany do

14

Strany počet

14

URL

https://link.springer.com/article/10.1007/s10032-021-00387-z

BibTex

@article{BUT175769,
  author="Oldřich {Kodym} and Michal {Hradiš}",
  title="TG2: text-guided transformer GAN for restoring document readability and perceived quality",
  journal="International Journal on Document Analysis and Recognition",
  year="2021",
  volume="2021",
  number="1",
  pages="1--14",
  doi="10.1007/s10032-021-00387-z",
  issn="1433-2825",
  url="https://link.springer.com/article/10.1007/s10032-021-00387-z"
}