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%.

翻译：科学文献包含大量复杂、非结构化、性质复杂的数字(即由多个图像、图表和图纸组成),将这些复合数字从这些数字中分离出来对于信息检索至关重要。在本文件中,我们提出一个复合数字分离的新战略,将复合数字分解成构成子图,同时保持子图及其各自的字幕组成部分之间的联系。我们提议一个两阶段框架,以解决拟议的复合图解分解问题。特别是,子图标签检测模块在第一阶段检测所有子图标签。然后,在子图解检测模块中,检测到的子图解标签有助于通过优化特征选择过程来检测子图,并作为额外特征提供全球布局信息。我们进行了广泛的实验,以验证拟议框架的有效性和优越性,使检测精确度提高9%。