We present a novel, open-access dataset designed for semantic layout analysis, built to support document recreation workflows through mapping with the Text Encoding Initiative (TEI) standard. This dataset includes 7,254 annotated pages spanning a large temporal range (1600-2024) of digitised and born-digital materials across diverse document types (magazines, papers from sciences and humanities, PhD theses, monographs, plays, administrative reports, etc.) sorted into modular subsets. By incorporating content from different periods and genres, it addresses varying layout complexities and historical changes in document structure. The modular design allows domain-specific configurations. We evaluate object detection models on this dataset, examining the impact of input size and subset-based training. Results show that a 1280-pixel input size for YOLO is optimal and that training on subsets generally benefits from incorporating them into a generic model rather than fine-tuning pre-trained weights.

翻译：我们提出了一种新颖的、开放获取的语义布局分析数据集，旨在通过与文本编码倡议（TEI）标准的映射来支持文档重建工作流。该数据集包含7,254个标注页面，涵盖了一个大的时间范围（1600-2024年）的数字化及原生数字材料，涉及多种文档类型（杂志、科学与人文领域的论文、博士学位论文、专著、剧本、行政报告等），并整理为模块化子集。通过纳入不同时期和体裁的内容，该数据集处理了文档结构中的不同布局复杂性和历史变迁。其模块化设计允许进行特定领域的配置。我们在此数据集上评估了目标检测模型，考察了输入尺寸和基于子集训练的影响。结果表明，对于YOLO模型，1280像素的输入尺寸是最优的，并且基于子集的训练通常受益于将其整合到一个通用模型中，而非对预训练权重进行微调。