Chart to Table

Chart-to-table conversion focuses on automatically transforming visual chart data into structured tabular formats, facilitating easier data analysis and reasoning by machines. Current research emphasizes improving the efficiency and accuracy of this conversion using multimodal large language models (MLLMs) and vision-language models (VLMs), often incorporating techniques like program-of-thoughts learning and improved chart representation methods to enhance numerical computation and reasoning capabilities. This research is significant because it enables more effective automated chart understanding, paving the way for applications in data analysis, fact-checking, and improved accessibility for individuals with visual impairments.