All

What are you looking for?

All
Projects
Results
Organizations

Quick search

  • Projects supported by TA ČR
  • Excellent projects
  • Projects with the highest public support
  • Current projects

Smart search

  • That is how I find a specific +word
  • That is how I leave the -word out of the results
  • “That is how I can find the whole phrase”

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

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • 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

    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