US researchers from the Massachusetts Institute of Technology (MIT) and the MIT-IBM Watson AI Lab have developed an open-source dataset called ChartNet, which contains 1.5 million chart samples spanning 24 chart types and six plotting libraries. The paper, ‘ChartNet: a million-scale, high-quality multimodal dataset for robust chart understanding’, was presented at June’s IEEE Computer Vision and Pattern Recognition Conference. The researchers noted that existing vision-language models can struggle with charts because they must combine visual, numerical and linguistic understanding. Models trained with ChartNet improved across chart reconstruction, data extraction, summarisation and chart question-answering. Lead author Jovana Kondic, an MIT electrical engineering and computer science graduate student, said the aim was to help smaller models achieve strong performance without requiring “infinite amounts of computation”.