  Extracting metadata from scientific papers can be considered a solved problem
in NLP due to the high accuracy of state-of-the-art methods. However, this does
not apply to German scientific publications, which have a variety of styles and
layouts. In contrast to most of the English scientific publications that follow
standard and simple layouts, the order, content, position and size of metadata
in German publications vary greatly among publications. This variety makes
traditional NLP methods fail to accurately extract metadata from these
publications. In this paper, we present a method that extracts metadata from
PDF documents with different layouts and styles by viewing the document as an
image. We used Mask R-CNN that is trained on COCO dataset and finetuned with
PubLayNet dataset that consists of ~200K PDF snapshots with five basic classes
(e.g. text, figure, etc). We refine-tuned the model on our proposed synthetic
dataset consisting of ~30K article snapshots to extract nine patterns (i.e.
author, title, etc). Our synthetic dataset is generated using contents in both
languages German and English and a finite set of challenging templates obtained
from German publications. Our method achieved an average accuracy of around
$90\%$ which validates its capability to accurately extract metadata from a
variety of PDF documents with challenging templates.
