Hybrid Cascade Point Search Network for High Precision Bar Chart Component Detection

Bar charts are commonly used for data visualization. One common form of chart distribution is in its image form. To enable machine comprehension of chart images, precise detection of chart components in chart images is a critical step.  Existing image object detection methods do not perform well in chart component detection which requires high boundary detection precision. And traditional rule-based approaches lack enough generalization ability. In order to address this problem, we design a novel two-stage component detection framework for bar charts that combines point-based and region-based ideas, by simulating the process that human creating bounding boxes for objects. The experiment on our labeled ChartDet dataset shows our method greatly improves the performance of chart object detection. We further extend our method to a general object detection task and get comparable performance.