DIVA-DAF: A Deep Learning Framework for Historical Document Image Analysis

In this paper, we introduce a new deep learning framework called DIVA-DAF. We have developed this framework to support our research on historical document image analysis tasks and to develop techniques to reduce the need for manually-labeled ground truth. We want to apply self-supervised learning techniques and use different kinds of training data. Our new framework aids us in performing rapid prototyping and reproducible experiments. We present a first semantic segmentation experiment on DIVA-HisDB using our framework, achieving state-of-the-art results. The DIVA-DAF framework is open-source, and we encourage other research groups to use it for their experiments.