  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.
