Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Historical Document Synthesis with Generative Adversarial Networks
University of Fribourg.
University of Fribourg.
University of Fribourg.
University of Fribourg.
Show others and affiliations
2019 (English)In: 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW), IEEE, 2019, Vol. 5, p. 146-151Conference paper, Published paper (Other academic)
Abstract [en]

This work tackles a particular image-to-image translation problem, where the goal is to transform an image from a source domain (modern printed electronic document) to a target domain (historical handwritten document). The main motivation of this task is to generate massive synthetic datasets of "historic" documents which can be used for the training of document analysis systems. By completing this task, it becomes possible to consider the generation of a tremendous amount of synthetic training data using only one single deep learning algorithm. Existing approaches for synthetic document generation rely on heuristics, or 2D and 3D geometric transformation-functions and are typically targeted at degrading the document. We tackle the problem of document synthesis and propose to train a particular form of Generative Adversarial Neural Networks, to learn a mapping function from an input image to an output image. With several experiments, we show that our algorithm generates an artificial historical document image that looks like a real historical document - for expert and non-expert eyes - by transferring the "historical style" to the classical electronic document.

Place, publisher, year, edition, pages
IEEE, 2019. Vol. 5, p. 146-151
Series
International Conference on Document Analysis and Recognition Workshops (ICDARW)
Keywords [en]
historical document, deep learning, document synthesis
National Category
Computer Sciences
Research subject
Machine Learning
Identifiers
URN: urn:nbn:se:ltu:diva-78265DOI: 10.1109/ICDARW.2019.40096ISI: 000518786800025Scopus ID: 2-s2.0-85097418425ISBN: 978-1-7281-5054-3 (electronic)OAI: oai:DiVA.org:ltu-78265DiVA, id: diva2:1420871
Conference
The Second International Workshop on Computational Document Forensics (ICDAR 2019 Workshop), 20-25 September, 2019, Sydney, Australia
Available from: 2020-04-01 Created: 2020-04-01 Last updated: 2021-12-13Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Liwicki, Marcus

Search in DiVA

By author/editor
Liwicki, Marcus
By organisation
Embedded Internet Systems Lab
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 247 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf