# CommonForms: A Large, Diverse Dataset for Form Field Detection

arXiv: https://arxiv.org/abs/2509.16506 · HTML: https://mlsanity.com/paper/2509.16506/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 26 references that are in the map.
- First posted on arXiv 20 Sep 2025.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://mlsanity.com/paper/1912.13318/) (2019)
- [LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking](https://mlsanity.com/paper/2204.08387/) (2022)
- [LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding](https://mlsanity.com/paper/2012.14740/) (2020)
- [PubLayNet: largest dataset ever for document layout analysis](https://mlsanity.com/paper/1908.07836/) (2019)
- [FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents](https://mlsanity.com/paper/1905.13538/) (2019)
- [DiT: Self-supervised Pre-training for Document Image Transformer](https://mlsanity.com/paper/2203.02378/) (2022)
- [DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis](https://mlsanity.com/paper/2206.01062/) (2022)
- [TableBank: A Benchmark Dataset for Table Detection and Recognition](https://mlsanity.com/paper/1903.01949/) (2019)
- [LayoutParser: A Unified Toolkit for Deep Learning Based Document Image Analysis](https://mlsanity.com/paper/2103.15348/) (2021)
- [Deep Visual Template-Free Form Parsing](https://mlsanity.com/paper/1909.02576/) (2019)
- [CCpdf: Building a High Quality Corpus for Visually Rich Documents from Web Crawl Data](https://mlsanity.com/paper/2304.14953/) (2023)
- [Form2Seq : A Framework for Higher-Order Form Structure Extraction](https://mlsanity.com/paper/2107.04419/) (2021)
- [The Newspaper Navigator Dataset: Extracting And Analyzing Visual Content from 16 Million Historic Newspaper Pages in Chronicling America](https://mlsanity.com/paper/2005.01583/) (2020)
- [Visual FUDGE: Form Understanding via Dynamic Graph Editing](https://mlsanity.com/paper/2105.08194/) (2021)
- [Revising FUNSD dataset for key-value detection in document images](https://mlsanity.com/paper/2010.05322/) (2020)
- [Multi-Modal Association based Grouping for Form Structure Extraction](https://mlsanity.com/paper/2107.04396/) (2021)

## Built on by (most cited first, 4 of 4)

- [AutoFormBench: Benchmark Dataset for Automating Form Understanding](https://mlsanity.com/paper/2603.29832/) (2026)
- [AcroMELD: Recovering Interactive PDF Forms with Structure-Aware Graph Set Transformers](https://mlsanity.com/paper/2608.22338/) (2026)
- [Mind the Gaps: A Curated Benchmark for Form Field Detection](https://mlsanity.com/paper/2609.23679/) (2026)
- [CRAFT: An Agentic Spreadsheet Form Filling System with Template Awareness](https://mlsanity.com/paper/2610.04437/) (2026)

## Abstract (arXiv)

This paper introduces CommonForms, a web-scale dataset for form field detection. It casts the problem of form field detection as object detection: given an image of a page, predict the location and type (Text Input, Choice Button, Signature) of form fields. The dataset is constructed by filtering Common Crawl to find PDFs that have fillable elements. Starting with 8 million documents, the filtering process is used to arrive at a final dataset of roughly 55k documents that have over 450k pages. Analysis shows that the dataset contains a diverse mixture of languages and domains; one third of the pages are non-English, and among the 14 classified domains, no domain makes up more than 25% of the dataset. In addition, this paper presents a family of form field detectors, FFDNet-Small and FFDNet-Large, which attain a very high average precision on the CommonForms test set. Each model cost less than 500 to train. Ablation results show that high-resolution inputs are crucial for high-quality form field detection, and that the cleaning process improves data efficiency over using all PDFs that have fillable fields in Common Crawl. A qualitative analysis shows that they outperform a popular, commercially available PDF reader that can prepare forms. Unlike the most popular commercially available solutions, FFDNet can predict checkboxes in addition to text and signature fields. This is, to our knowledge, the first large scale dataset released for form field detection, as well as the first open source models. The dataset, models, and code will be released at https://github.com/jbarrow/commonforms