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Dating ancient manuscripts using radiocarbon and AI-based writing style analysis.

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Titel: Dating ancient manuscripts using radiocarbon and AI-based writing style analysis.
Autoren: Popović, Mladen, Dhali, Maruf A., Schomaker, Lambert, van der Plicht, Johannes, Lund Rasmussen, Kaare, La Nasa, Jacopo, Degano, Ilaria, Perla Colombini, Maria, Tigchelaar, Eibert
Quelle: PLoS ONE; 6/4/2025, Vol. 20 Issue 6, p1-14, 14p
Schlagwörter: ARTIFICIAL intelligence, PALEOGRAPHY, CARBON isotopes, MANUSCRIPTS, FORECASTING
Abstract: Determining by means of palaeography the chronology of ancient handwritten manuscripts such as the Dead Sea Scrolls is essential for reconstructing the evolution of ideas, but there is an almost complete lack of date-bearing manuscripts. To overcome this problem, we present Enoch, an AI-based date-prediction model, trained on the basis of 24 14C-dated scroll samples. By applying Bayesian ridge regression on angular and allographic writing style feature vectors, Enoch could predict 14C-based dates with varied mean absolute errors (MAEs) of 27.9 to 30.7 years. In order to explore the viability of the character-shape based dating approach, the trained Enoch model then computed date predictions for 135 non-dated scrolls, aligning with 79% in palaeographic post-hoc evaluation. The 14C ranges and Enoch's style-based predictions are often older than traditionally assumed palaeographic estimates, leading to a new chronology of the scrolls and the re-dating of ancient Jewish key texts that contribute to current debates on Jewish and Christian origins. [ABSTRACT FROM AUTHOR]
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  Data: Dating ancient manuscripts using radiocarbon and AI-based writing style analysis.
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  Data: Determining by means of palaeography the chronology of ancient handwritten manuscripts such as the Dead Sea Scrolls is essential for reconstructing the evolution of ideas, but there is an almost complete lack of date-bearing manuscripts. To overcome this problem, we present Enoch, an AI-based date-prediction model, trained on the basis of 24 <superscript>14</superscript>C-dated scroll samples. By applying Bayesian ridge regression on angular and allographic writing style feature vectors, Enoch could predict <superscript>14</superscript>C-based dates with varied mean absolute errors (MAEs) of 27.9 to 30.7 years. In order to explore the viability of the character-shape based dating approach, the trained Enoch model then computed date predictions for 135 non-dated scrolls, aligning with 79% in palaeographic post-hoc evaluation. The <superscript>14</superscript>C ranges and Enoch's style-based predictions are often older than traditionally assumed palaeographic estimates, leading to a new chronology of the scrolls and the re-dating of ancient Jewish key texts that contribute to current debates on Jewish and Christian origins. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of PLoS ONE is the property of Public Library of Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: 6/4/2025
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