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

Understand

Visual chart recognition systems are gaining increasing attention due to the growing demand for automatically identifying table headers and values from chart images.

• Current methods rely on keypoint detection to estimate data element shapes in charts but suffer from grouping errors in post-processing.
• To address this issue, we propose ChartDETR, a transformer-based multi-shape detector that localizes keypoints at the corners of regular shapes to reconstruct multiple data elements in a single chart image.
• Our method predicts all data element shapes at once by introducing query groups in set prediction, eliminating the need for further postprocessing.