Charts are useful communication tools for the presentation of data in a visually appealing format that facilitates comprehension. There have been many studies dedicated to chart mining, which refers to the process of automatic detection, extraction and analysis of charts to reproduce the tabular data that was originally used to create them. By allowing access to data which might not be available in other formats, chart mining facilitates the creation of many downstream applications. This paper presents a comprehensive survey of approaches across all components of the automated chart mining pipeline, such as (i) automated extraction of charts from documents; (ii) processing of multi-panel charts; (iii) automatic image classifiers to collect chart images at scale; (iv) automated extraction of data from each chart image, for popular chart types as well as selected specialized classes; (v) applications of chart mining; and (vi) datasets for training and evaluation, and the methods that were used to build them. Finally, we summarize the main trends found in the literature and provide pointers to areas for further research in chart mining.

中文翻译：

---

图表挖掘：自动图表分析方法综述

图表是有用的交流工具，用于以视觉上吸引人的格式呈现数据，有助于理解。有很多研究致力于图表挖掘，它指的是自动检测、提取和分析图表以重现最初用于创建它们的表格数据的过程。通过允许访问其他格式可能无法获得的数据，图表挖掘有助于创建许多下游应用程序。本文对自动化图表挖掘管道的所有组件的方法进行了全面调查，例如 (i) 从文档中自动提取图表；(ii) 处理多面板图表；(iii) 自动图像分类器以按比例收集图表图像；(iv) 从每个图表图像中自动提取数据，适用于流行的图表类型以及选定的专业类；(v) 图表挖掘的应用；(vi) 用于训练和评估的数据集，以及用于构建它们的方法。最后，我们总结了文献中发现的主要趋势，并提供了图表挖掘进一步研究领域的指示。