A Data Driven Approach for Compound Figure Separation Using Convolutional Neural Networks

Understand

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.