发表机构
University of Rouen Normandy; INSA of Rouen Normandy; Teklia; Bibliothèque nationale de France（鲁昂诺曼底大学; 鲁昂诺曼底国立应用科学学院; 泰克莱亚公司; 法国国家图书馆）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对报纸图像复杂层次结构理解难题，提出模块化自底向上流程结合开源模型及新型端到端Tiramisu架构，利用其并行注意力机制实现多任务，还发布新数据集，实验证明两种方法有效，突出各自优势。
AI 中文摘要
由于报纸图像具有复杂的嵌套层次结构和密集的异构布局，理解报纸图像仍然是一项具有挑战性的任务。在本文中，我们探索了两种互补的方法来理解报纸结构。首先，我们提出了一个模块化的自底向上流程，它结合了先进的开源模型：用于布局检测的YOLO、用于阅读顺序预测的LayoutReader和用于文章分割的自定义算法。这种方法利用现有强大组件，同时保持灵活性和可解释性。其次，我们引入了Tiramisu（用于层次结构理解的分层Transformer），这是一种基于Transformer的新型端到端架构，通过迭代分层过程明确地对文档层次结构进行建模。Tiramisu使用高度并行的注意力机制进行章节和文章分离、块定位、语义分类和阅读顺序预测。最后，我们发布了Finlam La Liberté，这是一个专门用于评估历史报纸中层次信息检索的新数据集。实验结果证明了这两种方法在重建复杂报纸层次结构方面的有效性，比较分析突出了它们在可扩展文档数字化方面各自的优势。Tiramisu训练代码，包括合成报纸生成器，可在该https URL获取。
英文摘要
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.
CommentsAccepted at ICDAR 2026 Workshop on Historical Document Imaging and Processing (HIP)
