  We present a framework to generate synthetic historical documents with
precise ground truth using nothing more than a collection of unlabeled
historical images. Obtaining large labeled datasets is often the limiting
factor to effectively use supervised deep learning methods for Document Image
Analysis (DIA). Prior approaches towards synthetic data generation either
require expertise or result in poor accuracy in the synthetic documents. To
achieve high precision transformations without requiring expertise, we tackle
the problem in two steps. First, we create template documents with
user-specified content and structure. Second, we transfer the style of a
collection of unlabeled historical images to these template documents while
preserving their text and layout. We evaluate the use of our synthetic
historical documents in a pre-training setting and find that we outperform the
baselines (randomly initialized and pre-trained). Additionally, with visual
examples, we demonstrate a high-quality synthesis that makes it possible to
generate large labeled historical document datasets with precise ground truth.
