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arXiv · 2607.00596

Semantic-Guided Reading Order Reconstruction in Historical Armenian Newspapers with LLMs

Chahan Vidal-Gorène·Nadi Tomeh·Victoria Khurshudyan
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Abstract

This paper addresses reading order reconstruction in historical Armenian newspapers, which combine complex layouts with limited language resources. We introduce a new annotated dataset of 66 pages and compare geometric heuristics, YOLO-based layout parsing, an end-to-end document model ECLAIR, and a hybrid method combining semantic zone detection with a generative LLM. Our hybrid method achieves the lowest error rates of all evaluated approaches, reducing ordering errors by up to 76% over the strongest geometric baseline, and remains robust in multi-page settings and under noisy OCR. Rather than targeting production the method is designed as a data bootstrapping strategy enabling rapid annotation in highly under-resourced scenarios. Alongside the dataset, we release a specialized Tesseract OCR model for historical Armenian print.

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Chahan Vidal-Gorène, Nadi Tomeh, Victoria Khurshudyan. 2026-07-01. Semantic-Guided Reading Order Reconstruction in Historical Armenian Newspapers with LLMs. https://arxiv.org/abs/2607.00596

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