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Visual chart recognition systems are gaining increasing attention due to thegrowing demand for automatically identifying table headers and values fromchart images. Current methods rely on keypoint detection to estimate dataelement shapes in charts but suffer from grouping errors in post-processing. Toaddress this issue, we propose ChartDETR, a transformer-based multi-shapedetector that localizes keypoints at the corners of regular shapes toreconstruct multiple data elements in a single chart image. Our method predictsall data element shapes at once by introducing query groups in set prediction,eliminating the need for further postprocessing. This property allows ChartDETRto serve as a unified framework capable of representing various chart typeswithout altering the network architecture, effectively detecting data elementsof diverse shapes. We evaluated ChartDETR on three datasets, achievingcompetitive results across all chart types without any additional enhancements.For example, ChartDETR achieved an F1 score of 0.98 on Adobe Synthetic,significantly outperforming the previous best model with a 0.71 F1 score.Additionally, we obtained a new state-of-the-art result of 0.97 onExcelChart400k. The code will be made publicly available.

ChartDETR: A Multi-shape Detection Network for Visual Chart Recognition