# DIVA-DAF: A Deep Learning Framework for Historical Document Image Analysis Source: [DIVA-DAF: A Deep Learning Framework for Historical Document Image Analysis]() Collection index: [llms.txt]() Data updated: 2026-01-04T04:10:30.263Z Authors: Lars V\\"ogtlin, Anna Scius-Bertrand, Paul Maergner, Andreas Fischer, Rolf Ingold Published: 2022-01-20T17:02:46.000Z ## Abstract Deep learning methods have shown strong performance in solving tasks for historical document image analysis.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an open-source deep learning framework, DIVA-DAF, which is based on PyTorch Lightning and specifically designed for historical document analysis.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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Semantic Segmentation on DIVA-HisDB CB55 (test)](): Semantic segmentation; DIVA-HisDB CB55 (test); mIoU: 93.04. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()