From Pixels to Insights: A Survey on Automatic Chart Understanding in the Era of Large Foundation Models

Data visualization in the form of charts plays a pivotal role in data analysis, offering critical insights and aiding in informed decision-making. Automatic chart understanding has witnessed significant advancements with the rise of large foundation models in recent years. Foundation models, such as large language models (LLMs), have revolutionized various natural language processing (NLP) tasks and are increasingly being applied to chart understanding tasks. This survey paper provides a comprehensive overview of the recent developments, challenges, and future directions in chart understanding within the context of these foundation models. The paper begins by defining chart understanding, outlining problem formulations, and discussing fundamental building blocks crucial for studying chart understanding tasks. In the section on tasks and datasets, we explore various tasks within chart understanding and discuss their evaluation metrics and sources of both charts and textual inputs. Modeling strategies are then examined, encompassing both classification-based and generation-based approaches, along with tool augmentation techniques that enhance chart understanding performance. Furthermore, we discuss the state-of-the-art performance of each task and discuss how we can improve the performance. Challenges and future directions are addressed in a dedicated section, highlighting issues such as domain-specific charts, lack of efforts in evaluation, and agent-oriented settings. This survey paper serves to provide valuable insights and directions for future research in chart understanding leveraging large foundation models. The studies mentioned in this paper, along with emerging new research, will be continually updated at: https://github.com/khuangaf/Awesome-Chart-Understanding.