  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.
