  Accurate document layout analysis is a key requirement for high-quality PDF
document conversion. With the recent availability of public, large ground-truth
datasets such as PubLayNet and DocBank, deep-learning models have proven to be
very effective at layout detection and segmentation. While these datasets are
of adequate size to train such models, they severely lack in layout variability
since they are sourced from scientific article repositories such as PubMed and
arXiv only. Consequently, the accuracy of the layout segmentation drops
significantly when these models are applied on more challenging and diverse
layouts. In this paper, we present \textit{DocLayNet}, a new, publicly
available, document-layout annotation dataset in COCO format. It contains 80863
manually annotated pages from diverse data sources to represent a wide
variability in layouts. For each PDF page, the layout annotations provide
labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also
provides a subset of double- and triple-annotated pages to determine the
inter-annotator agreement. In multiple experiments, we provide baseline
accuracy scores (in mAP) for a set of popular object detection models. We also
demonstrate that these models fall approximately 10\% behind the
inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is
of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and
DocLayNet, showing that layout predictions of the DocLayNet-trained models are
more robust and thus the preferred choice for general-purpose document-layout
analysis.
