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We introduce DeepDIVA: an infrastructure designed to enable quick andintuitive setup of reproducible experiments with a large range of usefulanalysis functionality. Reproducing scientific results can be a frustratingexperience, not only in document image analysis but in machine learning ingeneral. Using DeepDIVA a researcher can either reproduce a given experimentwith a very limited amount of information or share their own experiments withothers. Moreover, the framework offers a large range of functions, such asboilerplate code, keeping track of experiments, hyper-parameter optimization,and visualization of data and results. To demonstrate the effectiveness of thisframework, this paper presents case studies in the area of handwritten documentanalysis where researchers benefit from the integrated functionality. DeepDIVAis implemented in Python and uses the deep learning framework PyTorch. It iscompletely open source, and accessible as Web Service through DIVAServices.

DeepDIVA: A Highly-Functional Python Framework for Reproducible Experiments