SpaDen : Sparse and Dense Keypoint Estimation for Real-World Chart
  Understanding
Saleem Ahmed Pengyu Yan David Doermann Srirangaraj Setlur Venu Govindaraju
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.