  With an abundance of research papers in deep learning, reproducibility or
adoption of the existing works becomes a challenge. This is due to the lack of
open source implementations provided by the authors. Further, re-implementing
research papers in a different library is a daunting task. To address these
challenges, we propose a novel extensible approach, DLPaper2Code, to extract
and understand deep learning design flow diagrams and tables available in a
research paper and convert them to an abstract computational graph. The
extracted computational graph is then converted into execution ready source
code in both Keras and Caffe, in real-time. An arXiv-like website is created
where the automatically generated designs is made publicly available for 5,000
research papers. The generated designs could be rated and edited using an
intuitive drag-and-drop UI framework in a crowdsourced manner. To evaluate our
approach, we create a simulated dataset with over 216,000 valid design
visualizations using a manually defined grammar. Experiments on the simulated
dataset show that the proposed framework provide more than $93\%$ accuracy in
flow diagram content extraction.
