We introduce a novel bottom-up approach for the extraction of chart data. Our
model utilizes images of charts as inputs and learns to detect keypoints (KP),
which are used to reconstruct the components within the plot area. Our novelty
lies in detecting a fusion of continuous and discrete KP as predicted heatmaps.
A combination of sparse and dense per-pixel objectives coupled with a uni-modal
self-attention-based feature-fusion layer is applied to learn KP embeddings.
Further leveraging deep metric learning for unsupervised clustering, allows us
to segment the chart plot area into various objects. By further matching the
chart components to the legend, we are able to obtain the data series names. A
post-processing threshold is applied to the KP embeddings to refine the object
reconstructions and improve accuracy. Our extensive experiments include an
evaluation of different modules for KP estimation and the combination of deep
layer aggregation and corner pooling approaches. The results of our experiments
provide extensive evaluation for the task of real-world chart data extraction.

我们提出了一种新的自底向上方法，用于提取图表数据。我们的模型利用图表的图像作为输入，并学习检测关键点（KP），用于重建绘图区域内的组件。我们的创新在于检测连续和离散 KP 的融合，作为预测热图。我们应用了一种稀疏和密集的像素目标组合，结合了单模自注意力特征融合层来学习 KP 嵌入。进一步利用无监督聚类的深度度量学习，将图表绘图区域分割成各种对象。通过将图表组件与图例进行匹配，我们可以获得数据系列名称。我们应用后处理阈值来提高 KP 嵌入的对象重建精度。我们广泛的实验证明了 KP 估计和深层聚合以及角点池化方法的不同模块的性能，为真实世界图表数据提取任务提供了广泛的评估结果。

SpaDen：用于真实世界图表理解的稀疏和密集关键点估计

SpaDen : Sparse and Dense Keypoint Estimation for Real-World Chart  Understanding

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。