  Data extraction from line-chart images is an essential component of the
automated document understanding process, as line charts are a ubiquitous data
visualization format. However, the amount of visual and structural variations
in multi-line graphs makes them particularly challenging for automated parsing.
Existing works, however, are not robust to all these variations, either taking
an all-chart unified approach or relying on auxiliary information such as
legends for line data extraction. In this work, we propose LineFormer, a robust
approach to line data extraction using instance segmentation. We achieve
state-of-the-art performance on several benchmark synthetic and real chart
datasets. Our implementation is available at
https://github.com/TheJaeLal/LineFormer .
