DocTrack: A Visually-Rich Document Dataset Really Aligned with Human Eye Movement for Machine Reading

TL;DR

This paper introduces DocTrack, a new visually-rich document dataset aligned with human eye movements, to improve machine reading and understanding of complex documents by mimicking human reading patterns.

Contribution

The creation of the DocTrack dataset aligned with eye-tracking data and analysis of human reading order's impact on document understanding tasks.

Findings

Current Document AI models still lag behind human reading accuracy.

Aligning machine reading with human eye movement improves understanding.

Human-like reading order influences model performance.

Abstract

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}.