Structure-Aware Text Recognition for Ancient Greek Critical Editions

• URL: http://arxiv.org/abs/2603.02803v1
• Date: Tue, 03 Mar 2026 09:42:43 GMT
• Title: Structure-Aware Text Recognition for Ancient Greek Critical Editions
• Authors: Nicolas Angleraud, Antonia Karamolegkou, Benoît Sagot, Thibault Clérice,
• Abstract summary: This paper investigates structure-aware text recognition for Ancient Greek critical editions.<n>We introduce a large-scale synthetic corpus of 185,000 page images generated from TEI/XML sources with controlled typographic and layout variation.<n>We evaluate three state-of-the-art visual language models under both zero-shot and fine-tuning regimes.
• Score: 16.43811675687955
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in visual language models (VLMs) have transformed end-to-end document understanding. However, their ability to interpret the complex layout semantics of historical scholarly texts remains limited. This paper investigates structure-aware text recognition for Ancient Greek critical editions, which have dense reference hierarchies and extensive marginal annotations. We introduce two novel resources: (i) a large-scale synthetic corpus of 185,000 page images generated from TEI/XML sources with controlled typographic and layout variation, and (ii) a curated benchmark of real scanned editions spanning more than a century of editorial and typographic practices. Using these datasets, we evaluate three state-of-the-art VLMs under both zero-shot and fine-tuning regimes. Our experiments reveal substantial limitations in current VLM architectures when confronted with highly structured historical documents. In zero-shot settings, most models significantly underperform compared to established off-the-shelf software. Nevertheless, the Qwen3VL-8B model achieves state-of-the-art performance, reaching a median Character Error Rate of 1.0\% on real scans. These results highlight both the current shortcomings and the future potential of VLMs for structure-aware recognition of complex scholarly documents.

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