# Revising FUNSD dataset for key-value detection in document images

arXiv: https://arxiv.org/abs/2010.05322 · HTML: https://mlsanity.com/paper/2010.05322/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2020.

## Built on by (most cited first, 10 of 10)

- [Doc2Graph: a Task Agnostic Document Understanding Framework based on Graph Neural Networks](https://mlsanity.com/paper/2208.11168/) (2022)
- [BuDDIE: A Business Document Dataset for Multi-task Information Extraction](https://mlsanity.com/paper/2404.04003/) (2024)
- [Transformers and Language Models in Form Understanding: A Comprehensive Review of Scanned Document Analysis](https://mlsanity.com/paper/2403.04080/) (2024)
- [Information Redundancy and Biases in Public Document Information Extraction Benchmarks](https://mlsanity.com/paper/2304.14936/) (2023)
- [CommonForms: A Large, Diverse Dataset for Form Field Detection](https://mlsanity.com/paper/2509.16506/) (2025)
- [TreeForm: End-to-end Annotation and Evaluation for Form Document Parsing](https://mlsanity.com/paper/2402.05282/) (2024)
- [KH-FUNSD: A Hierarchical and Fine-Grained Layout Analysis Dataset for Low-Resource Khmer Business Document](https://mlsanity.com/paper/2512.11849/) (2025)
- [A LayoutLMv3-Based Model for Enhanced Relation Extraction in Visually-Rich Documents](https://mlsanity.com/paper/2404.10848/) (2024)
- [Information Extraction from Documents: Question Answering vs Token Classification in real-world setups](https://mlsanity.com/paper/2304.10994/) (2023)
- [Revising RVL-CDIP: Quantifying Errors and Test-Train Overlap](https://mlsanity.com/paper/2606.31446/) (2026)

## Abstract (arXiv)

FUNSD is one of the limited publicly available datasets for information extraction from document im-ages. The information in the FUNSD dataset is defined by text areas of four categories ("key", "value", "header", "other", and "background") and connectivity between areas as key-value relations. In-specting FUNSD, we found several inconsistency in labeling, which impeded its applicability to thekey-value extraction problem. In this report, we described some labeling issues in FUNSD and therevision we made to the dataset. We also reported our implementation of for key-value detection onFUNSD using a UNet model as baseline results and an improved UNet model with Channel-InvariantDeformable Convolution.