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Unlocking history through automated virtual unfolding of sealed documents imaged by X-ray microtomography

Computational flattening algorithms have been successfully applied to X-ray microtomography scans of damaged historical documents, but have so far been limited to scrolls, books, and documents with one or two folds. The challenge tackled here is to reconstruct the intricate folds, tucks, and slits o...

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Autores principales: Dambrogio, Jana, Ghassaei, Amanda, Smith, Daniel Starza, Jackson, Holly, Demaine, Martin L., Davis, Graham, Mills, David, Ahrendt, Rebekah, Akkerman, Nadine, van der Linden, David, Demaine, Erik D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7925573/
https://www.ncbi.nlm.nih.gov/pubmed/33654094
http://dx.doi.org/10.1038/s41467-021-21326-w
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author Dambrogio, Jana
Ghassaei, Amanda
Smith, Daniel Starza
Jackson, Holly
Demaine, Martin L.
Davis, Graham
Mills, David
Ahrendt, Rebekah
Akkerman, Nadine
van der Linden, David
Demaine, Erik D.
author_facet Dambrogio, Jana
Ghassaei, Amanda
Smith, Daniel Starza
Jackson, Holly
Demaine, Martin L.
Davis, Graham
Mills, David
Ahrendt, Rebekah
Akkerman, Nadine
van der Linden, David
Demaine, Erik D.
author_sort Dambrogio, Jana
collection PubMed
description Computational flattening algorithms have been successfully applied to X-ray microtomography scans of damaged historical documents, but have so far been limited to scrolls, books, and documents with one or two folds. The challenge tackled here is to reconstruct the intricate folds, tucks, and slits of unopened letters secured shut with “letterlocking,” a practice—systematized in this paper—which underpinned global communications security for centuries before modern envelopes. We present a fully automatic computational approach for reconstructing and virtually unfolding volumetric scans of a locked letter with complex internal folding, producing legible images of the letter’s contents and crease pattern while preserving letterlocking evidence. We demonstrate our method on four letterpackets from Renaissance Europe, reading the contents of one unopened letter for the first time. Using the results of virtual unfolding, we situate our findings within a novel letterlocking categorization chart based on our study of 250,000 historical letters.
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spelling pubmed-79255732021-03-21 Unlocking history through automated virtual unfolding of sealed documents imaged by X-ray microtomography Dambrogio, Jana Ghassaei, Amanda Smith, Daniel Starza Jackson, Holly Demaine, Martin L. Davis, Graham Mills, David Ahrendt, Rebekah Akkerman, Nadine van der Linden, David Demaine, Erik D. Nat Commun Article Computational flattening algorithms have been successfully applied to X-ray microtomography scans of damaged historical documents, but have so far been limited to scrolls, books, and documents with one or two folds. The challenge tackled here is to reconstruct the intricate folds, tucks, and slits of unopened letters secured shut with “letterlocking,” a practice—systematized in this paper—which underpinned global communications security for centuries before modern envelopes. We present a fully automatic computational approach for reconstructing and virtually unfolding volumetric scans of a locked letter with complex internal folding, producing legible images of the letter’s contents and crease pattern while preserving letterlocking evidence. We demonstrate our method on four letterpackets from Renaissance Europe, reading the contents of one unopened letter for the first time. Using the results of virtual unfolding, we situate our findings within a novel letterlocking categorization chart based on our study of 250,000 historical letters. Nature Publishing Group UK 2021-03-02 /pmc/articles/PMC7925573/ /pubmed/33654094 http://dx.doi.org/10.1038/s41467-021-21326-w Text en © The Author(s) 2021, corrected publication 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Dambrogio, Jana
Ghassaei, Amanda
Smith, Daniel Starza
Jackson, Holly
Demaine, Martin L.
Davis, Graham
Mills, David
Ahrendt, Rebekah
Akkerman, Nadine
van der Linden, David
Demaine, Erik D.
Unlocking history through automated virtual unfolding of sealed documents imaged by X-ray microtomography
title Unlocking history through automated virtual unfolding of sealed documents imaged by X-ray microtomography
title_full Unlocking history through automated virtual unfolding of sealed documents imaged by X-ray microtomography
title_fullStr Unlocking history through automated virtual unfolding of sealed documents imaged by X-ray microtomography
title_full_unstemmed Unlocking history through automated virtual unfolding of sealed documents imaged by X-ray microtomography
title_short Unlocking history through automated virtual unfolding of sealed documents imaged by X-ray microtomography
title_sort unlocking history through automated virtual unfolding of sealed documents imaged by x-ray microtomography
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7925573/
https://www.ncbi.nlm.nih.gov/pubmed/33654094
http://dx.doi.org/10.1038/s41467-021-21326-w
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