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Conference papers

Deep-Learning Technology for Book Conservation Assessment in Libraries and Archives

Valérie Lee 1 Lahcen Yamoun 1 Michel Jordan 2 Camille Simon Simon Chane 2 David Picard 3 Julien Longhi 1, 4, 5 
5 IDHN - Institut des Humanités numériques
ETIS - UMR 8051 - Equipes Traitement de l'Information et Systèmes : UMR 8051, LT2D - Lexiques, Textes, Discours, Dictionnaire - Centre Jean Pruvost : EA 7518, MRTE - EA 4112 - Laboratoire Mobilités, Réseaux, Territoires, Environnements : EA 4112, AGORA - EA 7392 - Laboratoire AGORA : EA 7392
Abstract : One of the primary goals of Libraries and Archives is the conservation of their collections in order to pass them on to future generations. The large number of books kept in storage makes this task particularly challenging. Artificial intelligence offers the possibility of processing large volumes of data in a short period of time, but this technology has not been used for book conservation. A team of artificial intelligence scientists and one conservator is now developing a tool to automatically assess the conservation state of a binding at any given time.
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Submitted on : Friday, March 25, 2022 - 5:01:09 PM
Last modification on : Tuesday, September 6, 2022 - 3:28:54 PM
Long-term archiving on: : Sunday, June 26, 2022 - 7:10:35 PM


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  • HAL Id : hal-03620294, version 1


Valérie Lee, Lahcen Yamoun, Michel Jordan, Camille Simon Simon Chane, David Picard, et al.. Deep-Learning Technology for Book Conservation Assessment in Libraries and Archives. Un patrimoine pour l'avenir, une science pour le patrimoine, Mar 2022, Paris, France. ⟨hal-03620294⟩



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