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 Libert\'e, 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.
A max-regret inference rule is proposed that prioritizes commitments with high opportunity cost in reading order inference to avoid the cascading"edge-theft"failures of greedy edge selection.
Iddo Hakim, Sharva Gogawale, Omer Ventura et al.· 0 citations
The Institutional Newspapers Pipeline is presented, a modular system designed to extract high-quality, structured datasets from historical newspaper scans that was architected so that each step remains interpretable and customizable, and so that the pipeline as a whole remains computationally frugal enough to run on workstation-level hardware.
Matteo Cargnelutti, Catherine Brobston, Eben English et al.· 0 citations
This work introduces SON-1K, a comprehensive benchmark for text-to-image generation, and proposes a new approach, the enhanced LMDpp, enhancing the performance of the novel two-stage Large Language Model (LLM)-grounded diffusion model pipeline (LMD).
Weiyue Li, Yi Li, Xiaoyue Wang et al.· 0 citations
Multilingual documents encapsulate rich regional cultures, scientific discoveries, and historical records. Parsing this content into structured, machine-readable formats is critical for unlocking global knowledge. However, existing benchmarks predominantly focus on high-resource languages like English and Chinese, creating an evaluation blind spot concerning model performance on other languages. While recent Vision-Language Models (VLMs) claim support for hundreds of languages, the lack of ground truth makes it impossible to empirically verify these capabilities. To bridge this gap, we introduce MORE, a large-scale benchmark designed for multilingual document parsing evaluation. MORE distinguishes itself through three key dimensions: (1) Unprecedented Scale: It covers 149 languages, making it the most linguistically diverse benchmark to date; (2) Structural Complexity: Unlike previous works, it extends evaluation beyond plain text to include structural elements such as code blocks, tables, and catalogs; and (3) Data Authenticity: All samples are curated from real-world documents via a model-assisted, human-refined annotation pipeline. We evaluate state-of-the-art models using MORE, establishing new performance baselines for long-tail languages and validating the benchmark's effectiveness in diagnosing model capabilities in realistic, diverse scenarios. The MORE dataset will be available at https://github.com/zimoqingfeng/MORE.
Long Xu, Binghong Wu, Ting-Ting Yu et al.· 3 citations
By bridging the gap between visually complex documents and machine-readable knowledge, Docling provides a foundation for reliable document understanding in next-generation AI systems.
P. Staar· Proceedings of the 2026 ACM...· 0 citations
This paper addresses reading order reconstruction in historical Armenian newspapers with a new annotated dataset of 66 pages and compares 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.
Chahan Vidal-Gorène, Nadi Tomeh, Victoria Khurshudyan· 1 citation