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Commit a9f3a5ce authored by Ludovic Moncla's avatar Ludovic Moncla
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Update README.md

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......@@ -12,6 +12,7 @@ Ecole Normale Supérieure de Lyon, bâtiment Recherche (D4), salle D4 179 et en
Valeria Vitale (University of Sheffield)
[<img src="https://projet.liris.cnrs.fr/geode/seminaires-ixxi/session8_nov22/seminaire-session8-presentation1.png" width="650" />](https://projet.liris.cnrs.fr/geode/seminaires-ixxi/session8_nov22/seminaire-session8-presentation1.mp4 "Enregistrement présentation 1")
The Ordnance Survey (OS) is Great Britain’s national mapping agency. It was born in 1745, and its maps have played an important role in shaping the nation. This rich corpus of historical documents has been digitised, and it is today in large part available to the public. Not only these maps are valuable means to investigate past representations and perceptions of space, but they also show an uncommon feature that makes them especially interesting to archaeologists, historians and heritage specialists: OS maps record the location of antiquities, both extant and disappeared. This practice implies that, unlike many others, OS maps are committed to represent not only the visible features of the landscape, but even the invisible ones, blending different temporal layers in a single, complex document.
To deliver many layers of information, OS maps combine different codes, making the most of polysemic signs. In particular, a separate system to represent antiquities and historical sites relies on the combination of ad hoc labels, dedicated vocabulary, and special fonts.
......@@ -24,6 +25,8 @@ Machines Reading Maps is an international collaboration between the Alan Turing
Rainer Simon (Austrian Institute of Technology)
[<img src="https://geode.liris.cnrs.fr/seminaires-ixxi/session8_nov22/seminaire-session8-presentation2.png" width="650" />](https://geode.liris.cnrs.fr/seminaires-ixxi/session8_nov22/seminaire-session8-presentation2.mp4 "Enregistrement présentation 2")
Maps constitute a significant body of global cultural heritage, and the number of maps available digitally is increasing rapidly. The Machines Reading Maps project from The Alan Turing Institute, University of Minnesota, Austrian Institute of Technology, University of Sheffield, and the University of Southern California explores how Machine Learning can be used to extract text from maps, making cartographic collections more accessible and useful.
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