Algorithmic handwriting analysis of Judah's military correspondence sheds light on composition of biblical texts

Shira Faigenbaum-Golovin*, Arie Shaus, Barak Sober, David Levin, Nadav Na'aman, Benjamin Sass, Eli Turkel, Eli Piasetzky, Israel Finkelstein

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

34 Scopus citations


The relationship between the expansion of literacy in Judah and composition of biblical texts has attracted scholarly attention for over a century. Information on this issue can be deduced from Hebrew inscriptions from the final phase of the first Temple period. We report our investigation of 16 inscriptions from the Judahite desert fortress of Arad, dated ca. 600 BCE the Eve of Nebuchadnezzar's destruction of Jerusalem. The inquiry is based on new methods for image processing and document analysis, as well as machine learning algorithms. These techniques enable identification of the minimal number of authors in a given group of inscriptions. Our algorithmic analysis, complemented by the textual information, rEveals a minimum of six authors within the examined inscriptions. The results indicate that in this remote fort literacy had spread throughout the military hierarchy, down to the quartermaster and probably Even below that rank. This implies that an educational infrastructure that could support the composition of literary texts in Judah already existed before the destruction of the first Temple. A similar lEvel of literacy in this area is attested again only 400 y later, ca. 200 BCE.

Original languageEnglish
Pages (from-to)4664-4669
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number17
StatePublished - 26 Apr 2016


FundersFunder number
Seventh Framework Programme
European Research Council229418
Israel Science Foundation644/08, 1457/13
Tel Aviv University
Seventh Framework Programme


    • Arad ostraca
    • Biblical exegesis
    • Document analysis
    • Literacy level
    • Machine learning


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