Towards Hierarchical Structure Understanding of Newspaper Images

2026-07-16

AI Summary

This study addresses the challenges of document understanding in historical newspaper images, which exhibit complex nested structures and densely heterogeneous layouts. To tackle this problem, the authors propose two complementary approaches: a modular bottom-up pipeline integrating YOLO-based layout detection, LayoutReader for reading order prediction, and a custom article segmentation algorithm; and Tiramisu, a novel end-to-end hierarchical Transformer architecture that explicitly models the multi-level structure of newspapers through an iterative hierarchical mechanism. The work also contributes Finlam La Liberté, the first dataset tailored for hierarchical information retrieval in historical newspapers, along with open-sourced training code and a synthetic newspaper generator. Experimental results demonstrate that both methods effectively reconstruct intricate newspaper hierarchies, offering significant advantages for scalable document digitization.

Abstract

Understanding newspaper images remains a challenging task due to their complex, nested hierarchical structures and dense, heterogeneous layouts. In this paper, we explore two complementary approaches for newspaper structure understanding. First, we present a modular bottom-up pipeline that combines state-of-the-art open-source models: YOLO for layout detection, LayoutReader for reading order prediction, and a custom algorithm for article segmentation. This approach leverages existing robust components while maintaining flexibility and interpretability. Second, we introduce Tiramisu (Tiered Transformers for Hierarchical Structure Understanding), a novel end-to-end transformer-based architecture that explicitly models document hierarchy through an iterative tiered process. Tiramisu performs section and article separation, block localization, semantic categorization, and reading order prediction using highly parallelized attention mechanisms. Finally, we release Finlam La Liberté, a new dataset designed specifically for evaluating hierarchical information retrieval in historical newspapers. Experimental results demonstrate the effectiveness of both approaches in reconstructing complex newspaper hierarchies, with comparative analysis highlighting their respective strengths for scalable document digitization. The Tiramisu training code, including the synthetic newspaper generator, is available at https://git.litislab.fr/tiramisu/tiramisu-newspaper-articles-extractor.

Problem

• newspaper images
• hierarchical structure
• layout understanding
• document digitization
• information retrieval

Innovation

• hierarchical structure understanding
• Tiramisu
• transformer-based architecture
• newspaper layout analysis
• article segmentation