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 the first stage, the subfigure label detection module detects all subfigure labels. 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 precision by 9% compared to the benchmark.

No abstract

This is an Accepted Manuscript for the Microscopy and Microanalysis 2020 Proceedings. This version may be subject to change during the production process.

Different types of spectroscopies, such as X-ray absorption near edge structure (XANES) and Raman spectroscopy, play a very important role in analyzing the characteristics of different materials. In scientific literature,...

Scientific literature provides the authoritative data source for most scientific fields. Numerous microscopy images rich in information, in particular, are contained within scientific publications. The retrieval of these imaging data currently requires a keyword search and labor-intensive human reading of individual articles. An efficient image retrieval tool would be significantly beneficial for researchers.