Journal Article

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

Schomaker, Lambert, Perla Colombini, Maria, van der Plicht, Johannes, Tigchelaar, Eibert, Popović, Mladen, Lund Rasmussen, Kaare, La Nasa, Jacopo, Dhali, Maruf A., Degano, Ilaria

2025

Overview

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 14 C-dated scroll samples. By applying Bayesian ridge regression on angular and allographic writing style feature vectors, Enoch could predict 14 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 14 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.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: 2nd century / 4th century / Analysis / Archaeology - methods / Artificial Intelligence / Bayes Theorem / Bayesian analysis / Biology and Life Sciences / Calibration / Carbon 14 / Centuries / Computer and Information Sciences / Datasets / Dating / Handwriting / History, Ancient / Humans / Manuscripts as Topic - history / Manuscripts, Hebrew / Medicine and Health Sciences / Paleography / Pattern recognition / Physical Sciences / Prediction models / Radiocarbon dating / Radiometric dating / Radiometric Dating - methods / Research and Analysis Methods / Scrolls / Social Sciences / Writing