  A key problem in automatic analysis and understanding of scientific papers is
to extract semantic information from non-textual paper components like figures,
diagrams, tables, etc. Much of this work requires a very first preprocessing
step: decomposing compound multi-part figures into individual subfigures.
Previous work in compound figure separation has been based on manually designed
features and separation rules, which often fail for less common figure types
and layouts. Moreover, few implementations for compound figure decomposition
are publicly available.
  This paper proposes a data driven approach to separate compound figures using
modern deep Convolutional Neural Networks (CNNs) to train the separator in an
end-to-end manner. CNNs eliminate the need for manually designing features and
separation rules, but require a large amount of annotated training data. We
overcome this challenge using transfer learning as well as automatically
synthesizing training exemplars. We evaluate our technique on the ImageCLEF
Medical dataset, achieving 85.9% accuracy and outperforming previous
techniques. We have released our implementation as an easy-to-use Python
library, aiming to promote further research in scientific figure mining.
