# Structure-Aware Text Recognition for Ancient Greek Critical Editions

arXiv: https://arxiv.org/abs/2603.02803 · HTML: https://mlsanity.com/paper/2603.02803/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Document understanding and multimodal RAG, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 3 Mar 2026.

## Placed in

- Multimodal LLMs / vision-language models / [Document understanding and multimodal RAG](https://mlsanity.com/mllm/document-understanding-and-multimodal-rag/)

## Builds on (most cited first, 6 of 6)

- [Qwen3-VL Technical Report](https://mlsanity.com/paper/2511.21631/) (2025)
- [OmniBench: Towards The Future of Universal Omni-Language Models](https://mlsanity.com/paper/2409.15272/) (2024)
- [LightOnOCR: A 1B End-to-End Multilingual Vision-Language Model for State-of-the-Art OCR](https://mlsanity.com/paper/2601.14251/) (2026)
- [Lost in OCR Translation? Vision-Based Approaches to Robust Document Retrieval](https://mlsanity.com/paper/2505.05666/) (2025)
- [Benchmarking Vision-Language Models on Optical Character Recognition in Dynamic Video Environments](https://mlsanity.com/paper/2502.06445/) (2025)
- [Page Layout Analysis of Text-heavy Historical Documents: a Comparison of Textual and Visual Approaches](https://mlsanity.com/paper/2212.13924/) (2022)

## Built on by (most cited first, 5 of 5)

- [Reading or Guessing? Visual Grounding Failures of Vision-Language Models for OCR in Ancient Greek Editions](https://mlsanity.com/paper/2605.27750/) (2026)
- [End-to-End Text Line Detection and Ordering](https://mlsanity.com/paper/2606.04166/) (2026)
- [Stoicheia: Character-Level Masked Diffusion for Ancient Greek Textual Restoration, Parsing, and Metrical Scansion](https://mlsanity.com/paper/2608.07249/) (2026)
- [Combining Synthetic and Real Data for Low-Resource Historical OCR: A Manchu Case Study](https://mlsanity.com/paper/2609.11495/) (2026)
- [Which papyrus HTR is good enough? Character-error-rate tolerance of four papyrological tasks on Greek texts](https://mlsanity.com/paper/2609.37755/) (2026)

## Abstract (arXiv)

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.