DeepDIVA: A Highly-Functional Python Framework for Reproducible Experiments

We introduce DeepDIVA: an infrastructure designed to enable quick and intuitive setup of reproducible experiments with a large range of useful analysis functionality. Reproducing scientific results can be a frustrating experience, not only in document image analysis but in machine learning in general. Using DeepDIVA a researcher can either reproduce a given experiment with a very limited amount of information or share their own experiments with others. Moreover, the framework offers a large range of functions, such as boilerplate code, keeping track of experiments, hyper-parameter optimization, and visualization of data and results. To demonstrate the effectiveness of this framework, this paper presents case studies in the area of handwritten document analysis where researchers benefit from the integrated functionality. DeepDIVA is implemented in Python and uses the deep learning framework PyTorch. It is completely open source, and accessible as Web Service through DIVAServices.Comment: Submitted at the 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), 6 pages, 6 Figure

Web Services in Document Image Analysis: Recent Developments on DIVAServices and the Importance of Building an Ecosystem

Web Services are being adapted into the workflows of many Document Image Analysis researchers. However, so far, there is no common platform for providing access to algorithms in the community. D IVA Services aims to become this by providing a platform that is open to the whole community to provide their own methods as Web Services. In this paper we present updates and enhancements made to the existing D IVA Services platform. This includes a new computational backend, a revamped execution workflow based on asynchronous communication, and the possibility for methods to specify their outputs. Furthermore, we discuss the importance of an ecosystem for such platforms. We argue that only providing a RESTful API is not enough. Users need tools and services around the framework that support them in adapting the Web Services and we introduce some of the tools that we built around D IVA Services