  Deep learning methods have shown strong performance in solving tasks for
historical document image analysis. However, despite current libraries and
frameworks, programming an experiment or a set of experiments and executing
them can be time-consuming. This is why we propose an open-source deep learning
framework, DIVA-DAF, which is based on PyTorch Lightning and specifically
designed for historical document analysis. Pre-implemented tasks such as
segmentation and classification can be easily used or customized. It is also
easy to create one's own tasks with the benefit of powerful modules for loading
data, even large data sets, and different forms of ground truth. The
applications conducted have demonstrated time savings for the programming of a
document analysis task, as well as for different scenarios such as pre-training
or changing the architecture. Thanks to its data module, the framework also
allows to reduce the time of model training significantly.
