New Dataset Targets Low-Resource Handwritten Text Recognition for Ancient Coptic Manuscripts

Researchers have introduced SCAM, a new dataset designed for Handwritten Text Recognition (HTR) of Sahidic Coptic ancient manuscripts. This dataset addresses the challenges of low-resource languages, rare scripts, and degraded historical documents, combining heterogeneous acquisition conditions with typical manuscript degradations like ink fading and material deterioration. Benchmarking current state-of-the-art HTR approaches on SCAM highlights their limitations in low-resource, historically grounded scenarios, providing a benchmark for future developments in the field. AI

IMPACT This dataset could advance research in low-resource HTR, potentially improving AI's ability to process historical and underrepresented languages.

RANK_REASON The cluster is about a new academic paper introducing a dataset for a specific research task. [lever_c_demoted from research: ic=1 ai=1.0]

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• Sahidic Coptic
• Silvia Cascianelli PhD

• paper
• other

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