mlsanityData to 7 Oct 2026

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

arXiv 2310.14802 ↗First posted 23 Oct 2023cs.HC cs.CV cs.IRcited by 2

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Abstract (arXiv)

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 textscDocTrack, 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 urlhttps://github.com/hint-lab/doctrack.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 19 of its 36 references that are in the map. First posted on arXiv 23 Oct 2023.

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19 of 36 references.

References · 19

  1. 2019LayoutLM: Pre-training of Text and Layout for Document Image Understandingcited by 549
  2. 2022LayoutLMv3: Pre-training for Document AI with Unified Text and Image Maskingcited by 481
  3. 2020LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understandingcited by 406
  4. 2019FUNSD: A Dataset for Form Understanding in Noisy Scanned Documentscited by 345
  5. 2021StrucTexT: Structured Text Understanding with Multi-Modal Transformerscited by 90
  6. 2019Graph Convolution for Multimodal Information Extraction from Visually Rich Documentscited by 88
  7. 2021LayoutReader: Pre-training of Text and Layout for Reading Order Detectioncited by 79
  8. 2020PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networkscited by 70
  9. 2022XYLayoutLM: Towards Layout-Aware Multimodal Networks For Visually-Rich Document Understandingcited by 68
  10. 2021Towards Robust Visual Information Extraction in Real World: New Dataset and Novel Solutioncited by 64
  11. 2020Improving Natural Language Processing Tasks with Human Gaze-Guided Neural Attentioncited by 45
  12. 2023StrucTexTv2: Masked Visual-Textual Prediction for Document Image Pre-trainingcited by 39
7 more
  1. 2019Entity Recognition at First Sight: Improving NER with Eye Movement Informationcited by 26
  2. 2021ROPE: Reading Order Equivariant Positional Encoding for Graph-based Document Information Extractioncited by 22
  3. 2017Leveraging Cognitive Features for Sentiment Analysiscited by 19
  4. 2022PP-StructureV2: A Stronger Document Analysis Systemcited by 18
  5. 2020Generating Image Descriptions via Sequential Cross-Modal Alignment Guided by Human Gazecited by 16
  6. 2021CogAlign: Learning to Align Textual Neural Representations to Cognitive Language Processing Signalscited by 11
  7. 2020Relevance Prediction from Eye-movements Using Semi-interpretable Convolutional Neural Networkscited by 6

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