  Scientific literature contains large volumes of complex, unstructured figures
that are compound in nature (i.e. composed of multiple images, graphs, and
drawings). Separation of these compound figures is critical for information
retrieval from these figures. In this paper, we propose a new strategy for
compound figure separation, which decomposes the compound figures into
constituent subfigures while preserving the association between the subfigures
and their respective caption components. We propose a two-stage framework to
address the proposed compound figure separation problem. In particular, the
subfigure label detection module detects all subfigure labels in the first
stage. Then, in the subfigure detection module, the detected subfigure labels
help to detect the subfigures by optimizing the feature selection process and
providing the global layout information as extra features. Extensive
experiments are conducted to validate the effectiveness and superiority of the
proposed framework, which improves the detection precision by 9%.
