TreeForm: End-to-end Annotation and Evaluation for Form Document Parsing
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Abstract (arXiv)
Visually Rich Form Understanding (VRFU) poses a complex research problem due to the documents' highly structured nature and yet highly variable style and content. Current annotation schemes decompose form understanding and omit key hierarchical structure, making development and evaluation of end-to-end models difficult. In this paper, we propose a novel F1 metric to evaluate form parsers and describe a new content-agnostic, tree-based annotation scheme for VRFU: TreeForm. We provide methods to convert previous annotation schemes into TreeForm structures and evaluate TreeForm predictions using a modified version of the normalized tree-edit distance. We present initial baselines for our end-to-end performance metric and the TreeForm edit distance, averaged over the FUNSD and XFUND datasets, of 61.5 and 26.4 respectively. We hope that TreeForm encourages deeper research in annotating, modeling, and evaluating the complexities of form-like documents.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 27 of its 44 references that are in the map. First posted on arXiv 7 Feb 2024.
Builds on
27 of 44 references.
References · 27
- 2020DocVQA: A Dataset for VQA on Document Imagescited by 1,288
- 2020TAPAS: Weakly Supervised Table Parsing via Pre-trainingcited by 432
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- 2021ICDAR2019 Competition on Scanned Receipt OCR and Information Extractioncited by 254
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- 2021StructuralLM: Structural Pre-training for Form Understandingcited by 91
15 more
- 2021StrucTexT: Structured Text Understanding with Multi-Modal Transformerscited by 90
- 2022Unified Pretraining Framework for Document Understandingcited by 85
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- 2020Spatial Dependency Parsing for Semi-Structured Document Information Extractioncited by 65
- 2023DocILE Benchmark for Document Information Localization and Extractioncited by 58
- 2019Deep Visual Template-Free Form Parsingcited by 24
- 2021ICDAR 2021 Competition on Document VisualQuestion Answeringcited by 19
- 2022Bi-VLDoc: Bidirectional Vision-Language Modeling for Visually-Rich Document Understandingcited by 18
- 2021Form2Seq : A Framework for Higher-Order Form Structure Extractioncited by 16
- 2021Visual FUDGE: Form Understanding via Dynamic Graph Editingcited by 15
- 2021Cost-effective End-to-end Information Extraction for Semi-structured Document Imagescited by 14
- 2022XDoc: Unified Pre-training for Cross-Format Document Understandingcited by 12
- 2020Revising FUNSD dataset for key-value detection in document imagescited by 10
- 2021Multi-Modal Association based Grouping for Form Structure Extractioncited by 6
- 2021Field Extraction from Forms with Unlabeled Datacited by 4
Built on by
Citing papers per year: 2024: 1; 2025: 1; 2026: 1.
3 papers.
- 2024BuDDIE: A Business Document Dataset for Multi-task Information Extractioncited by 8
- 2025Where is this coming from? Making groundedness count in the evaluation of Document VQA modelscited by 4
- 2026Beyond Human Annotation: Recent Advances in Data Generation Methods for Document Intelligencecited by 4
- 2026Beyond Human Annotation: Recent Advances in Data Generation Methods for Document Intelligence18 Jan 2026
- 2025Where is this coming from? Making groundedness count in the evaluation of Document VQA models24 Mar 2025
- 2024BuDDIE: A Business Document Dataset for Multi-task Information Extraction5 Apr 2024