DocTrack: A Visually-Rich Document Dataset Really Aligned with Human Eye
  Movement for Machine Reading
Hao Wang Qingxuan Wang Yue Li Changqing Wang Chenhui Chu Rui Wang
The use of visually-rich documents (VRDs) in various fields has created a
demand for Document AI models that can read and comprehend documents like
humans, which requires the overcoming of technical, linguistic, and cognitive
barriers. Unfortunately, the lack of appropriate datasets has significantly
hindered advancements in the field. To address this issue, we introduce
\textsc{DocTrack}, a VRD dataset really aligned with human eye-movement
information using eye-tracking technology. This dataset can be used to
investigate the challenges mentioned above. Additionally, we explore the impact
of human reading order on document understanding tasks and examine what would
happen if a machine reads in the same order as a human. Our results suggest
that although Document AI models have made significant progress, they still
have a long way to go before they can read VRDs as accurately, continuously,
and flexibly as humans do. These findings have potential implications for
future research and development of Document AI models. The data is available at
\url{https://github.com/hint-lab/doctrack}.