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arXiv · 2606.15987

A Text Recognition Dataset from Sahidic Coptic Ancient Manuscripts

Fabio Quattrini·Carmine Zaccagnino·Costanza Bianchi·Silvia Cascianelli·Rita Cucchiara
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Abstract

In this work, we target Handwritten Text Recognition (HTR) in low-resource scenarios, which arise from underrepresented languages, rare scripts, and degraded visual conditions typical of historical documents. We introduce SCAM (Sahidic Coptic Ancient Manuscripts), a new line-level dataset built from digitized ancient manuscripts written in the extinct Sahidic Coptic dialect. The dataset reflects a realistic and challenging setting, as it combines heterogeneous acquisition conditions across libraries with typical manuscript degradations such as ink fading, bleed-through, and material deterioration. In addition to visual complexity, SCAM poses significant linguistic challenges due to the scarcity of resources for Sahidic Coptic, its uncommon alphabet, and dialect-specific diacritics. To support research in low-resource HTR, we benchmark several state-of-the-art approaches based on different paradigms, highlighting their limitations and strengths in this setting. Our results underline the gap between current HTR performance on well-resourced modern scripts and historically grounded, low-resource scenarios, thus providing a reference point for future developments.

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Fabio Quattrini, Carmine Zaccagnino, Costanza Bianchi, Silvia Cascianelli, Rita Cucchiara. 2026-09-01. A Text Recognition Dataset from Sahidic Coptic Ancient Manuscripts. https://doi.org/10.1007/978-3-032-36023-6_16

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