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Automated Sign Detection Across the Electronic Babylonian Library: A Large-Scale Dataset and End-to-End Cuneiform OCR Pipeline

MCML Authors

Abstract

Learning to read cuneiform tablets is an extremely demanding task; consequently, of the roughly half million excavated tablets, only a small fraction has been analysed by Assyriologists. Computer vision offers a promising avenue for decipherment but requires large, densely annotated datasets. To address this limitation, the largest annotated cuneiform sign dataset to date is used, and a Deformable Detection Transformer (DETR)-based object detection model is evaluated under two class granularities of 173 and 106 classes. The proposed system integrates automatic tablet-side extraction, heuristic line grouping, and n-gram-based textual similarity evaluation to bridge visual sign detection and textual structure, and achieves consistent improvements of up to 28-37% over prior work on COCO-style detection metrics. At inference, the method is applied to 87,668 tablet fragments from the Electronic Babylonian Library (eBL) corpus, producing nearly 2.9 million sign detections. Although the approach operates without linguistic priors and remains sensitive to tablet damage and layout variability, it provides a scalable and interpretable foundation for corpus-wide cuneiform analysis and supports future integration with multimodal and linguistic modelling frameworks.

misc CAN+26

Preprint

Jun. 2026

Authors

W. Che • E. G. Arias • A. Niaz • A. Bender • E. Jiménez

Links

arXiv GitHub

Research Area

 A1 | Statistical Foundations & Explainability

BibTeXKey: CAN+26

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