DIVA-DAF: A Deep Learning Framework for Historical Document Image Analysis

TL;DR

DIVA-DAF is an open-source deep learning framework based on PyTorch Lightning, designed to simplify and accelerate historical document image analysis tasks like segmentation and classification.

Contribution

It introduces a customizable, easy-to-use framework with pre-implemented tasks and modules that significantly reduce development and training time for historical document analysis.

Findings

Time savings in programming document analysis tasks

Reduced model training time due to efficient data modules

Flexibility in customizing tasks and architectures

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Deep learning methods have shown strong performance in solving tasks for historical document image analysis.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an open-source deep learning framework, DIVA-DAF, which is based on PyTorch Lightning and specifically designed for historical document analysis.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.