Rescued from the dusty interior of the Qumran Caves in 1947, the Dead Sea Scrolls contain the oldest manuscripts of the Old Testament and are a crucial piece of Biblical history that dates back to the 4th century BCE.
But despite these scrolls’ status as an unmovable piece of religious history, there are still many things that scholars don’t really know about their origin. For example, who actually wrote them down?
Using artificial intelligence and pattern recognition, a team of paleographers (scientists who study ancient handwriting) and computer scientists from the University of Groningen have now discovered hidden details in these scrolls that point toward not just one scribe, but two original scribes.
The research was published Wednesday in the journal PLOS One.
What’s new — Previous archaeological attempts to analyze these texts, including incredibly finicky human-based visual analysis, had almost always come to the conclusion that there was a single scribe who put to paper all of the Dead Sea Scrolls, but this new study is one of the first to push back against that assumption.
“Pattern recognition and artificial intelligence techniques can assist researchers by processing large amounts of data and by producing quantitative analyses that are impossible for a human to perform,” write the authors.
To make this more-than-human analysis, the team focused on extracting “Hinge” and “fraglet” data — that is, information about the motion of the writing via character curvature and the shape of the characters, respectively.
“This study sheds new light on the Bible’s ancient scribal culture by providing new, tangible evidence that ancient biblical texts were not copied by a single scribe only but that multiple scribes, while carefully mirroring another scribe’s writing style,” the authors write.
Why it matters — The big question plaguing paleographers studying these ancient texts is how to, in essence, place themselves in the shoes of ancient scribes. This can help them understand whether or not a text was written by a single person whose style varied over time (due to tiredness or changing environments and utensils etc.) or multiple scribes attempting (but slightly failing) to mimic the same handwriting style.
Finding a better way to answer this question can not only shed light on the origins of the Dead Sea Scrolls but on any number of other ancient texts as well.
“Pattern recognition and artificial intelligence techniques do not give certainty of identification but they give statistical probabilities that can help the human expert understand and also decide between the likelihood of different possibilities,” write the authors.
What they did — In order to pull out patterns hidden within the handwriting, the research team used deep learning image processing (a type of machine learning where the A.I. looks for visual patterns in data) on over 2,000 digital images taken of the scrolls themselves and performed three types of “feature,” or specific detail, extraction from the processed images, including:
- Textural feature extraction using pattern recognition
- Allographic (e.g. shape variants) feature extraction using a neural network
- Adjoined feature (which is a combination of textural and allographic features)
The authors explain that textural features are more closely related to how the words were physically written (e.g. “micro-details along the ink trace”) — making it more similar to Hinge — while allographic details are related to the shape of the characters — making it more similar to fraglets.
Such minuscule differences in how a text is written have been historically challenging to identify in ancient texts, but the authors write that collecting data using machine learning can help make these details easier to understand and spot.
“The data relates directly to the tangible evidence of the ink traces in the scrolls, ink penned by scribes,” the authors explain. “As writing is a moving process that involves muscle movements of the hand and arm it is determined by the rules of physics and can therefore be quantified.”
Following their initial A.I. analysis, the researchers also conducted a second and third level of analysis using statistical analysis and good old visual analysis to confirm their initial findings.
Through their multi-tier analysis, the researchers identified some persistent differences between the writing up until column 27 of the text and from column 28 onwards — made more notable by a lacuna (or, a purposefully blank section of the manuscript) separating the sections.
Because these feature differences appeared to align directly with this column break, instead of randomly across the text, the authors write there’s only one conclusion they could draw from the data, “The presence of two scribes in [text] better explains the combined data concerning the fraglet and allographic levels of handwriting.”
“The two scribes show different writing patterns: we have demonstrated, on the basis of variance of the Fraglet distances, that the second scribe shows more variable writing patterns,” the authors continue.
What’s next — Scholars may never know for certain who — or how many people — wrote the Dead Sea Scrolls, but the authors write that a crucial outcome of their research is a demonstration that similar handwriting across a text doesn’t necessarily point toward a single scribe.
Instead, it may be the work of several, similarly trained scribes attempting to capture a uniform style. This insight could help reveal many more secrets hidden in plain sight, such as texts from the Nag Hammadi library or illuminated Gothic manuscripts like the “Northumberland Bestiary.”
“Instead of asking whether traditional palaeography really captures everything, our study shows the need for and added value of collaboration between the disciplines,” the authors write. “This may also apply to other ancient corpora that face similar palaeographic challenges, such as ancient Greek manuscripts.”
Abstract: The Dead Sea Scrolls are tangible evidence of the Bible’s ancient scribal culture. This study takes an innovative approach to palaeography—the study of ancient handwriting—as a new entry point to access this scribal culture. One of the problems of palaeography is to determine writer identity or difference when the writing style is near uniform. This is exemplified by the Great Isaiah Scroll (1QIsaa). To this end, we use pattern recognition and artificial intelligence techniques to innovate the palaeography of the scrolls and to pioneer the microlevel of individual scribes to open access to the Bible’s ancient scribal culture. We report new evidence for a breaking point in the series of columns in this scroll. Without prior assumption of writer identity, based on point clouds of the reduced-dimensionality feature space, we found that columns from the first and second halves of the manuscript ended up in two distinct zones of such scatter plots, notably for a range of digital palaeography tools, each addressing very different featural aspects of the script samples. In a secondary, independent, analysis, now assuming writer difference and using yet another independent feature method and several different types of statistical testing, a switching point was found in the column series. A clear phase transition is apparent in columns 27–29. We also demonstrated a difference in distance variances such that the variance is higher in the second part of the manuscript. Given the statistically significant differences between the two halves, a tertiary, post-hoc analysis was performed using visual inspection of character heatmaps and of the most discriminative Fraglet sets in the script. Demonstrating that two main scribes, each showing different writing patterns, were responsible for the Great Isaiah Scroll, this study sheds new light on the Bible’s ancient scribal culture by providing new, tangible evidence that ancient biblical texts were not copied by a single scribe only but that multiple scribes, while carefully mirroring another scribe’s writing style, could closely collaborate on one particular manuscript.
Original post: https://www.inverse.com/innovation/ai-dead-sea-scrolls