As a prerequisite of chart data extraction, the accurate detection of chart
basic elements is essential and mandatory. In contrast to object detection in
the general image domain, chart element detection relies heavily on context
information as charts are highly structured data visualization formats. To
address this, we propose a novel method CACHED, which stands for Context-Aware
Chart Element Detection, by integrating a local-global context fusion module
consisting of visual context enhancement and positional context encoding with
the Cascade R-CNN framework. To improve the generalization of our method for
broader applicability, we refine the existing chart element categorization and
standardized 18 classes for chart basic elements, excluding plot elements. Our
CACHED method, with the updated category of chart elements, achieves
state-of-the-art performance in our experiments, underscoring the importance of
context in chart element detection. Extending our method to the bar plot
detection task, we obtain the best result on the PMC test dataset.

本研究提出一种称作 CACHED 的方法，利用基于上下文信息的局部 - 全局融合模块，将视觉和位置信息编码与 Cascade R-CNN 框架相结合，实现了基本元素检测的目标，具有较强的普适性。该方法可应用于条形图检测任务，并在 PMC 测试数据集上取得最佳结果。