The University of Groningen and KU Leuven ERC project "The Hands that Wrote the Bible: Digital Palaeography and Scribal Culture of the Dead Sea Scrolls" has produced its first publication, A Digital Palaeographic Approach towards Writer Identification in the Dead Sea Scrolls. Congratulations to Maruf, Sheng, Mladen, Eibert, and Lambert for a job well done, as well as to Ruwan van der Iest for all of his behind-the-scenes work on this pilot project. This article was an attempt to use digital images of the Dead Sea Scrolls to determine how accurately existing digital tools are able to distinguish the scripts of samples from a limited number of scribes from different parts of documents and across different documents considered by paleographers to have been written by the same scribe. The computer ranks all samples in relation to a query sample and produces a ranked hitlist of samples that most closely match the query sample. Overall competence in automated handwriting recognition peaked at 80% correct identification of the scribe in the first position in the hitlist. The ability of the computer to correctly place samples of the scribe within the top 10 of the hitlist peaked at about 95%. These results will provide an important benchmark as we now seek to increase our precision by using the higher-quality IAA images and tailoring the measured features better to our documents. The field of paleography of the Dead Sea Scrolls is now well on its way to becoming truly digital, and it will be exiting to see the results over the next couple of years.
To understand the historical context of an ancient manuscript, scholars rely on the prior knowledge of writer and date of that document. In this paper, we study the Dead Sea Scrolls, a collection of ancient manuscripts with immense historical, religious, and linguistic significance, which was discovered in the mid-20th century near the Dead Sea. Most of the manuscripts of this collection have become digitally available only recently and techniques from the pattern recognition field can be applied to revise existing hypotheses on the writers and dates of these scrolls. This paper presents our ongoing work which aims to introduce digital palaeography to the field and generate fresh empirical data by means of pattern recognition and artificial intelligence. Challenges in analyzing the Dead Sea Scrolls are highlighted by a pilot experiment identifying the writers using several dedicated features. Finally, we discuss whether to use specifically-designed shape features for writer identifica tion or to use the Deep Learning methods on a relatively limited ancient manuscript collection which is degraded over the course of time and is not labeled, as in the case of the Dead Sea Scrolls.