Computerized Paleographic Investigation of Hebrew Iron Age Ostraca

Faigenbaum-Golovin, Shira; Shaus, Arie; Sober, Barak; Finkelstein, Israel; Levin, David; Moinester, M. A.; Piasetzky, E.; Turkel, Eli

doi:10.2458/azu_rc.57.18565

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…However, the documented corpora cover only limited linguistic and temporal diversity subsets. Many are limited to less than 50,000 examples [24,35,36]. These datasets also suffer from metadata inconsistencies, class imbalance, unrealistic data, and inadequate diversity.…”

Section: Training Datasets For Ancient Scriptsmentioning

confidence: 99%

Deep Aramaic: Towards a synthetic data paradigm enabling machine learning in epigraphy

Aioanei,

Hunziker-Rodewald,

Klein

et al. 2024

PLoS ONE

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Epigraphy is witnessing a growing integration of artificial intelligence, notably through its subfield of machine learning (ML), especially in tasks like extracting insights from ancient inscriptions. However, scarce labeled data for training ML algorithms severely limits current techniques, especially for ancient scripts like Old Aramaic. Our research pioneers an innovative methodology for generating synthetic training data tailored to Old Aramaic letters. Our pipeline synthesizes photo-realistic Aramaic letter datasets, incorporating textural features, lighting, damage, and augmentations to mimic real-world inscription diversity. Despite minimal real examples, we engineer a dataset of 250 000 training and 25 000 validation images covering the 22 letter classes in the Aramaic alphabet. This comprehensive corpus provides a robust volume of data for training a residual neural network (ResNet) to classify highly degraded Aramaic letters. The ResNet model demonstrates 95% accuracy in classifying real images from the 8th century BCE Hadad statue inscription. Additional experiments validate performance on varying materials and styles, proving effective generalization. Our results validate the model’s capabilities in handling diverse real-world scenarios, proving the viability of our synthetic data approach and avoiding the dependence on scarce training data that has constrained epigraphic analysis. Our innovative framework elevates interpretation accuracy on damaged inscriptions, thus enhancing knowledge extraction from these historical resources.

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Section: Training Datasets For Ancient Scriptsmentioning

confidence: 99%

Deep Aramaic: Towards a synthetic data paradigm enabling machine learning in epigraphy

Aioanei,

Hunziker-Rodewald,

Klein

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…First, the poor state of preservation of the ostraca ( Fig. 2) could not be remedied by existing image acquisition methods (9,10). Second, the imperfect digital images present a challenge for image segmentation and enhancement methods (11,12).…”

Section: Algorithmic Apparatusmentioning

confidence: 99%

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

Faigenbaum-Golovin

Shaus

Sober

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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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. B ased on biblical exegesis and historical considerations scholars debate whether the first major phase of compilation of biblical texts in Jerusalem took place before or after the destruction of the city by the Babylonians in 586 BCE (e.g., ref. 1). A related-and also disputed-issue is the level of literacy, that is, the basic ability to communicate in writing, especially in the Hebrew kingdoms of Israel and Judah (2). The best way to answer this question is to look at the material evidence: the corpus of inscriptions that originated from archaeological excavations (e.g., ref.3). Inscriptions citing biblical texts, or related to them, are rarely found (for two Jerusalem amulets possibly dating to this period, echoing the priestly blessing in Numbers 6:23-26, see refs. 4 and 5), probably because papyrus and parchment are not well preserved in the climate of the region. However, ostraca (inscriptions in ink on ceramic sherds) that deal with more mundane issues can also shed light on the volume and quality of writing and on the recognition of the power of the written word in the society.To explore the degree of literacy and stage setting for compilation of literary texts in monarchic Judah, we turned to Hebrew ostraca from the final days of the kingdom, before its destruction by Nebuchadnezzar in 586 BCE and the deportation of its elite to Babylonia. Several corpora of inscriptions exist for this period. We focused on the corpus of over 100 Hebrew ostraca found at the fortress of Arad, located in arid southern Judah, on the border of the kingdom with Edom (see ref. 6 and Fig.

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New Light on Six Inscriptions from Arad

Na’aman

2021

Tel Aviv

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Computerized Paleographic Investigation of Hebrew Iron Age Ostraca

Cited by 4 publications

References 12 publications

Deep Aramaic: Towards a synthetic data paradigm enabling machine learning in epigraphy

Deep Aramaic: Towards a synthetic data paradigm enabling machine learning in epigraphy

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

New Light on Six Inscriptions from Arad

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