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

Jana Dambrogio (), Amanda Ghassaei (), Daniel Starza Smith, Holly Jackson, Martin L. Demaine, Graham Davis (), David Mills, Rebekah Ahrendt, Nadine Akkerman, David van der Linden and Erik D. Demaine
Additional contact information
Jana Dambrogio: Wunsch Conservation Laboratory, Massachusetts Institute of Technology (MIT) Libraries
Amanda Ghassaei: Adobe Research
Daniel Starza Smith: Virginia Woolf Building
Holly Jackson: Massachusetts Institute of Technology
Martin L. Demaine: Massachusetts Institute of Technology
Graham Davis: Institute of Dentistry
David Mills: Institute of Dentistry
Rebekah Ahrendt: Utrecht University, Department of Media and Culture Studies
Nadine Akkerman: Leiden University Centre for the Arts in Society (LUCAS), Faculty of Humanities/English Literature
David van der Linden: Radboud University, Faculty of Arts, Department of History, Art History and Classics
Erik D. Demaine: Massachusetts Institute of Technology

Nature Communications, 2021, vol. 12, issue 1, 1-10

Abstract: Abstract Computational flattening algorithms have been successfully applied to X-ray microtomography scans of damaged historical documents, but have so far been limited to scrolls, books, and documents with one or two folds.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.

Date: 2021
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DOI: 10.1038/s41467-021-21326-w

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