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

2026-07-01

AI Summary

This study addresses the challenge of reconstructing reading order in historical Armenian newspapers, which is hindered by complex layouts and scarce linguistic resources. The authors propose a hybrid approach that integrates semantic region detection with a generative large language model (LLM). Their method combines geometric heuristics, YOLO-based layout parsing, the ECLAIR end-to-end model, and a semantic-LLM architecture, alongside a newly released Tesseract OCR model tailored for this domain. This framework effectively handles noisy OCR outputs and multi-page scenarios. As the first work to incorporate semantic guidance with LLMs for reading order reconstruction in low-resource settings, it significantly improves robustness, reducing ordering errors by up to 76% compared to the strongest geometric baseline, and offers a data-bootstrapping strategy to accelerate annotation workflows.

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.

Problem

• reading order reconstruction
• historical newspapers
• Armenian
• document layout analysis
• low-resource languages

Innovation

• reading order reconstruction
• semantic-guided layout analysis
• large language models (LLMs)
• historical document processing
• low-resource OCR