  One of the most pressing problems in the automated analysis of historical
documents is the availability of annotated training data. The problem is that
labeling samples is a time-consuming task because it requires human expertise
and thus, cannot be automated well. In this work, we propose a novel method to
construct synthetic labeled datasets for historical documents where no
annotations are available. We train a StyleGAN model to synthesize document
images that capture the core features of the original documents. While
originally, the StyleGAN architecture was not intended to produce labels, it
indirectly learns the underlying semantics to generate realistic images. Using
our approach, we can extract the semantic information from the intermediate
feature maps and use it to generate ground truth labels. To investigate if our
synthetic dataset can be used to segment the text in historical documents, we
use it to train multiple supervised segmentation models and evaluate their
performance. We also train these models on another dataset created by a
state-of-the-art synthesis approach to show that the models trained on our
dataset achieve better results while requiring even less human annotation
effort.
