DeepDIVA is an infrastructure designed to enable and intuitive setup of reproducible experiments with a range of useful analysis functionality. Reproducing scientific 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 with a very limited amount of information or share their own experiments with others. Moreover, the framework 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.
This work is under continue development and its initial release has been published at 16th International Conference on in Handwriting Recognition (ICFHR'18). A version of the original paper can be found here.