Figures visually represent an essential piece of information and provide an effective means to communicate scientific facts. Recently there have been many efforts toward extracting data directly from figures, specifically from tables, diagrams, and plots, using different Artificial Intelligence and Machine Learning techniques. This is because removing information from figures could lead to deeper insights into the concepts highlighted in the scientific documents. In this survey paper, we systematically categorize figures into five classes - tables, photos, diagrams, maps, and plots, and subsequently present a critical review of the existing methodologies and data sets that address the problem of figure classification. Finally, we identify the current research gaps and provide possible directions for further research on figure classification.

翻译：图像以视觉形式呈现关键信息，为科学事实的交流提供了有效途径。近年来，研究人员利用多种人工智能和机器学习技术，致力于直接从图像（特别是表格、示意图和图表）中提取数据。这是因为从图像中提取信息有助于更深入地理解科学文档中强调的概念。本综述论文将图像系统性地分为五类——表格、照片、示意图、地图和图表，随后针对现有的图像分类方法及数据集进行了批判性评述。最后，我们指出了当前的研究空白，并为图像分类的进一步研究提供了可能的方向。