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

TL;DR

This research shows how Enoch's quantitative, probability-based approach can be a tool for palaeographers and historians, re-dating ancient Jewish key texts and contributing to current debates on Jewish and Christian origins.

Abstract

Determining the chronology of ancient handwritten manuscripts is essential for reconstructing the evolution of ideas. For the Dead Sea Scrolls, this is particularly important. However, there is an almost complete lack of date-bearing manuscripts evenly distributed across the timeline and written in similar scripts available for palaeographic comparison. Here, we present Enoch, a state-of-the-art AI-based date-prediction model, trained on the basis of new radiocarbon-dated samples of the scrolls. Enoch uses established handwriting-style descriptors and applies Bayesian ridge regression. The challenge of this study is that the number of radiocarbon-dated manuscripts is small, while current machine learning requires an abundance of training data. We show that by using combined angular and allographic writing style feature vectors and applying Bayesian ridge regression, Enoch could predict the radiocarbon-based dates from style, supported by leave-one-out validation, with varied MAEs of 27.9 to 30.7 years relative to the radiocarbon dating. Enoch was then used to estimate the dates of 135 unseen manuscripts, revealing that 79 per cent of the samples were considered 'realistic' upon palaeographic post-hoc evaluation. We present a new chronology of the scrolls. The radiocarbon ranges and Enoch's style-based predictions are often older than the traditionally assumed palaeographic estimates. In the range of 300-50 BCE, Enoch's date prediction provides an improved granularity. The study is in line with current developments in multimodal machine-learning techniques, and the methods can be used for date prediction in other partially-dated manuscript collections. This research shows how Enoch's quantitative, probability-based approach can be a tool for palaeographers and historians, re-dating ancient Jewish key texts and contributing to current debates on Jewish and Christian origins.

Figures (31)

• Figure 1: Overview of date estimations by three information sources and a calendar date: (accepted) 2$\sigma$ calibrated ranges 14C (blue), Enoch (green), palaeography (red), and historical (black). The vertical axis contains the manuscript numbers, and the horizontal axis contains dates: BCE in negative and CE in positive.
• Figure 2: a, from full spectrum colour image to binarized image to 14C plot for 4Q259 that went into the training of Enoch. b, from full spectrum colour image to binarized image to Enoch’s date prediction plot for 4Q319 (see also Fig. \ref{['fig:4Q319proccess']}). Red bars represent the probability of each date bin. The blue curve shows the smoothed distribution. Grey spikes indicate the local uncertainty of the estimate.
• Figure 3: Enoch’s date prediction plots for 6 of the 54 columns from the two halves of 1QIsaa (the left 3 columns are from the first half of the manuscript, the right 3 columns are from the second half of the manuscript).
• Figure 4: The selection of 30 manuscript samples according to their traditional palaeographic date estimates.
• Figure 5: OxCal plot for Mur19 with red vertical line indicating the calendar date 71/72 CE.