论文标题
Diva-DAF：历史文档图像分析的深度学习框架
DIVA-DAF: A Deep Learning Framework for Historical Document Image Analysis
论文作者
论文摘要
深度学习方法在解决历史文档图像分析的任务方面表现出了强大的绩效。但是，尽管当前的库和框架，对实验或一组实验进行编程可能会很耗时。这就是为什么我们建议开源深度学习框架Diva-Daf，该框架基于Pytorch Lightning，并专门设计用于历史文档分析。可以轻松使用或定制的预先实施任务，例如细分和分类。创建自己的任务也很容易获得强大的模块来加载数据，甚至大型数据集以及不同形式的地面真相。进行的应用程序证明了文档分析任务的编程以及不同方案（例如预训练或更改体系结构）节省了时间。由于其数据模块，该框架还允许大大减少模型培训的时间。
Deep learning methods have shown strong performance in solving tasks for historical document image analysis.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an open-source deep learning framework, DIVA-DAF, which is based on PyTorch Lightning and specifically designed for historical document analysis.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.
