DIVA-DAF: A Deep Learning Framework for Historical Document Image Analysis
This work addresses efficiency issues for researchers and practitioners in historical document analysis, though it is incremental as it builds on existing libraries like PyTorch Lightning.
The authors tackled the time-consuming process of programming and executing experiments in historical document image analysis by proposing DIVA-DAF, an open-source deep learning framework based on PyTorch Lightning, which demonstrated time savings in programming tasks and model training.
Deep learning methods have shown strong performance in solving tasks for historical document image analysis.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an open-source deep learning framework, DIVA-DAF, which is based on PyTorch Lightning and specifically designed for historical document analysis.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.