11 Comité des travaux historiques et scientifiques
12 Service Expérimentation et Développement [Paris Rocquencourt]
The study of watermarks is a key step for archivists and historians as it enables them to reveal the origin of paper. Although highly practical, automatic watermark recognition comes with many difficultiesand is still considered an unsolved challenge. Nonetheless, Shen et al. [2019] recently introduced a newapproach for this specific task which showed promising results. Building upon this approach, this workproposes a new public web application dedicated to automatic watermark recognition entitled Filigranespour tous. The application not only hosts a detailed catalog of more than 17k watermarks manually collected from the French National Archives (Minutier central) or extracted from existing online resources(Briquet database), but it also enables non-specialists to identify a watermark from a simple photographin a few seconds. Moreover, additional watermarks can easily be added by the users making the enrichment of the existing catalog possible through crowdsourcing. Our Web application is available athttp://filigranes.inria.fr/.
Rubrique : Déluge de données : quelles compétences pour quelles données ?
Publié le : 17 juillet 2020
Accepté le : 15 juillet 2020
Soumis le : 26 mars 2020
Mots-clés : cross-domain recognition,deep learning,watermark recognition,web application,paper analysis,[SHS.MUSEO]Humanities and Social Sciences/Cultural heritage and museology
Financement :
Source : OpenAIRE Graph
Exploitation des bases d'images patrimoniales; Financeur: French National Research Agency (ANR); Code: ANR-17-CE23-0008
Références bibliographiques
2 Documents citant cet article
Utsab Saha;Sawradip Saha;Shaikh Anowarul Fattah;Mohammad Saquib, 2024, Npix2Cpix: A GAN-Based Image-to-Image Translation Network With Retrieval- Classification Integration for Watermark Retrieval From Historical Document Images, IEEE Access, 12, pp. 95857-95870, 10.1109/access.2024.3424662, https://doi.org/10.1109/access.2024.3424662.
Xi Shen;Robin Champenois;Shiry Ginosar;Ilaria Pastrolin;Morgane Rousselot;et al., 2022, Spatially-Consistent Feature Matching and Learning for Heritage Image Analysis, 130, 5, pp. 1325-1339, 10.1007/s11263-022-01576-x, https://hal.science/hal-03620996.