Chart Image

Research on chart image understanding focuses on automatically extracting information from charts and answering questions about their data, bridging the gap between visual and textual information. Current efforts leverage multimodal large language models (MLLMs), often incorporating techniques like visual question answering (VQA) and program-of-thoughts learning, to improve accuracy and efficiency in tasks such as chart-to-text summarization, fact-checking, and accessibility improvements for visually impaired users. This field is crucial for automating data analysis, enhancing information accessibility, and combating misinformation spread through misleading visualizations. The development of robust and efficient chart understanding models has significant implications for various fields, including scientific research, business intelligence, and data journalism.