Synthesis in Style: Semantic Segmentation of Historical Documents using Synthetic Data. (arXiv:2107.06777v2 [cs.CV] UPDATED)

Web: http://arxiv.org/abs/2107.06777

One of the most pressing problems in the automated analysis of historical
documents is the availability of annotated training data.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construct synthetic labeled datasets for historical documents where no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. …

arxiv cv data segmentation semantic synthetic data