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DIVA-DAF: A Deep Learning Framework for Historical Document Image Analysis

2022-01-20 17:02:46
Lars Vögtlin, Paul Maergner, Rolf Ingold

Abstract

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 We have developed this framework to support our research on historical document image analysis tasks and to develop techniques to reduce the need for manually-labeled ground truth.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.

Abstract (translated)

URL

https://arxiv.org/abs/2201.08295

PDF

https://arxiv.org/pdf/2201.08295.pdf


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