TG2: text-guided transformer GAN for restoring document readability and perceived quality
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F21%3APU142892" target="_blank" >RIV/00216305:26230/21:PU142892 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/article/10.1007/s10032-021-00387-z" target="_blank" >https://link.springer.com/article/10.1007/s10032-021-00387-z</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s10032-021-00387-z" target="_blank" >10.1007/s10032-021-00387-z</a>
Alternative languages
Result language
angličtina
Original language name
TG2: text-guided transformer GAN for restoring document readability and perceived quality
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
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
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
International Journal on Document Analysis and Recognition
ISSN
1433-2833
e-ISSN
1433-2825
Volume of the periodical
2021
Issue of the periodical within the volume
1
Country of publishing house
DE - GERMANY
Number of pages
14
Pages from-to
1-14
UT code for WoS article
000698372200001
EID of the result in the Scopus database
2-s2.0-85115335316