The correct detection of article layout in historical newspaper pages remains challenging but is important for Natural Language Processing ( NLP) and machine learning applications in the field of digital history. Digital newspaper portals typically provide Optical Character Recognition ( OCR) text, albeit of varying quality. Unfortunately, layout information is often missing, limiting this rich source's scope. Our dataset is designed to address this issue for historic German-language newspapers. The Chronicling Germany dataset contains 581 annotated historical newspaper pages from the time period between 1852 and 1924. Historic domain experts have spent more than 1,500 hours annotating the dataset. The paper presents a processing pipeline and establishes baseline results on in- and out-of-domain test data using this pipeline. Both our dataset and the corresponding baseline code are freely available online. This work creates a starting point for future research in the field of digital history and historic German language newspaper processing. Furthermore, it provides the opportunity to study a low-resource task in computer vision.

翻译：在历史报纸页面中准确检测文章布局仍然具有挑战性，但对于数字历史领域的自然语言处理（NLP）和机器学习应用至关重要。数字报纸门户通常提供光学字符识别（OCR）文本，但其质量参差不齐。遗憾的是，布局信息经常缺失，限制了这一丰富资源的应用范围。我们的数据集旨在解决历史德语报纸的这一难题。Chronicling Germany数据集包含1852年至1924年间581页带注释的历史报纸页面。历史领域专家花费了超过1500小时对该数据集进行注释。本文提出了一条处理流程，并利用该流程在领域内和领域外测试数据上建立了基线结果。我们的数据集及相应的基线代码均可在网上免费获取。这项工作为数字历史和德语历史报纸处理领域的未来研究奠定了基础，同时为计算机视觉中的低资源任务研究提供了契机。