# DIVA-DAF: A Deep Learning Framework for Historical Document Image Analysis

arXiv: https://arxiv.org/abs/2201.08295 · HTML: https://mlsanity.com/paper/2201.08295/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 52 references that are in the map.
- First posted on arXiv 20 Jan 2022.

## Builds on (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/1902.06162/) (2019)
- [OpenML: networked science in machine learning](https://mlsanity.com/paper/1407.7722/) (2014)
- [LayoutParser: A Unified Toolkit for Deep Learning Based Document Image Analysis](https://mlsanity.com/paper/2103.15348/) (2021)
- [A Framework For Contrastive Self-Supervised Learning And Designing A New Approach](https://mlsanity.com/paper/2009.00104/) (2020)
- [Chainer: A Deep Learning Framework for Accelerating the Research Cycle](https://mlsanity.com/paper/1908.00213/) (2019)
- [A Comprehensive Study of ImageNet Pre-Training for Historical Document Image Analysis](https://mlsanity.com/paper/1905.09113/) (2019)
- [Labeling, Cutting, Grouping: an Efficient Text Line Segmentation Method for Medieval Manuscripts](https://mlsanity.com/paper/1906.11894/) (2019)
- [Handwritten Text Segmentation via End-to-End Learning of Convolutional Neural Network](https://mlsanity.com/paper/1906.05229/) (2019)
- [Open Evaluation Tool for Layout Analysis of Document Images](https://mlsanity.com/paper/1712.01656/) (2017)
- [Self-Supervised Representation Learning on Document Images](https://mlsanity.com/paper/2004.10605/) (2020)
- [Generating Synthetic Handwritten Historical Documents With OCR Constrained GANs](https://mlsanity.com/paper/2103.08236/) (2021)
- [DocEmul: a Toolkit to Generate Structured Historical Documents](https://mlsanity.com/paper/1710.03474/) (2017)
- [A Pitfall of Unsupervised Pre-Training](https://mlsanity.com/paper/1712.01655/) (2017)
- [DeepDIVA: A Highly-Functional Python Framework for Reproducible Experiments](https://mlsanity.com/paper/1805.00329/) (2018)
- [Machine Learning at Scale](https://mlsanity.com/paper/1402.6076/) (2014)
- [Improving Reproducible Deep Learning Workflows with DeepDIVA](https://mlsanity.com/paper/1906.04736/) (2019)
- [DeepZensols: Deep Natural Language Processing Framework](https://mlsanity.com/paper/2109.03383/) (2021)

## Abstract (arXiv)

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.