Artificial intelligence based writer identification generates new evidence for the unknown scribes of the Dead Sea Scrolls exemplified by the Great Isaiah Scroll (1QIsaa)
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Abstract (arXiv)
The Dead Sea Scrolls are tangible evidence of the Bible's ancient scribal culture. Palaeography - the study of ancient handwriting - can provide access to this scribal culture. However, one of the problems of traditional palaeography is to determine writer identity when the writing style is near uniform. This is exemplified by the Great Isaiah Scroll (1QIsaa). To this end, we used pattern recognition and artificial intelligence techniques to innovate the palaeography of the scrolls regarding writer identification and to pioneer the microlevel of individual scribes to open access to the Bible's ancient scribal culture. Although many scholars believe that 1QIsaa was written by one scribe, 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 around column 27. Given the statistically significant differences between the two halves, a tertiary, post-hoc analysis was performed. Demonstrating that two main scribes 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 could closely collaborate on one particular manuscript.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Oct 2020.
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Citing papers per year: 2022: 2; 2023: 2; 2024: 2; 2025: 0; 2026: 2.
8 papers.
- 2024Opportunities and Challenges of Large Language Models for Low-Resource Languages in Humanities Researchcited by 26
- 2022Writer Retrieval and Writer Identification in Greek Papyricited by 6
- 2023A Statistical Exploration of Text Partition Into Constituents: The Case of the Priestly Source in the Books of Genesis and Exoduscited by 3
- 2022Image-based material analysis of ancient historical documentscited by 1
- 2023Deep Aramaic: Towards a Synthetic Data Paradigm Enabling Machine Learning in Epigraphycited by 1
- 2026Multi-Expert Routing for Multi-Domain Low-Resource OCR: A Manchu Case Studycited by 1
- 2024Segmentation of Ink and Parchment in Dead Sea Scroll Fragmentscited by 0
- 2026Leveraging Morphology for Historical Script Metrological Analysiscited by 0
- 2026Multi-Expert Routing for Multi-Domain Low-Resource OCR: A Manchu Case Study15 Jul 2026
- 2026Leveraging Morphology for Historical Script Metrological Analysis8 Jun 2026
- 2024Opportunities and Challenges of Large Language Models for Low-Resource Languages in Humanities Research30 Nov 2024
- 2024Segmentation of Ink and Parchment in Dead Sea Scroll Fragments16 Nov 2024
- 2023Deep Aramaic: Towards a Synthetic Data Paradigm Enabling Machine Learning in Epigraphy11 Oct 2023
- 2023A Statistical Exploration of Text Partition Into Constituents: The Case of the Priestly Source in the Books of Genesis and Exodus3 May 2023
- 2022Writer Retrieval and Writer Identification in Greek Papyri15 Dec 2022
- 2022Image-based material analysis of ancient historical documents2 Mar 2022