{"ID":5551824,"CreatedAt":"2026-07-02T01:54:51.863792489Z","UpdatedAt":"2026-07-04T08:17:08.509157724Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.00596","arxiv_id":"2607.00596","title":"Semantic-Guided Reading Order Reconstruction in Historical Armenian Newspapers with LLMs","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.","short_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 combi...","url_abs":"https://arxiv.org/abs/2607.00596","url_pdf":"https://arxiv.org/pdf/2607.00596v1","authors":"[\"Chahan Vidal-Gorène\",\"Nadi Tomeh\",\"Victoria Khurshudyan\"]","published":"2026-07-01T08:19:00Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Large Language Model\"]","has_code":false}