研究报纸结构理解的，有开源Tiramisu代码和新数据集，对文档数字化领域很实用。
该论文研究了报纸图像的层级结构理解难题，提出两种互补方法：一是基于YOLO进行布局检测、LayoutReader进行阅读顺序预测、自定义算法进行文章分割的模块化流水线；二是端到端Transformer架构Tiramisu（Tiered Transformers），通过迭代分层注意力机制实现章节分离、块定位、语义分类和阅读顺序预测。论文还发布了新数据集Finlam La Liberté，专门用于历史报纸层级信息检索的评估。实验证明两种方法均能有效重建复杂报纸层级结构。
Towards Hierarchical Structure Understanding of Newspaper Images
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.