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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>CommonForms: A Large, Diverse Dataset for Form Field Detection · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2509.16506/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2509.16506/feed.xml" rel="self" type="application/rss+xml" /><item><title>CommonForms: A Large, Diverse Dataset for Form Field Detection — HN submission</title><link>https://news.ycombinator.com/item?id=45635530</link><guid isPermaLink="false">urn:hackerpapers:submission:2509.16506:45635530</guid><pubDate>Sun, 19 Oct 2025 16:31:52 GMT</pubDate><description>&lt;p&gt;Show HN: Browser-based PDF form fields detection (YOLO-based)&lt;/p&gt;&lt;p&gt;Hey HN! Last week, Joe Barrow released CommonForms [1], a set of open models for automatically detecting form fields in PDFs. He trained two models, FFDNet-S and FFDNet-L, on a dataset of 55k documents. You can read more about his approach in the arXiv paper [2]. As someone who&amp;#x27;s been searching for reliable models to auto-detect form fields (one of the last hard problems in PDF form filling), I was seriously impressed by the quality of these models. I wanted to give them the attention and distribution they deserve, so I created a fully browser-based implementation that handles both detection…&lt;/p&gt;&lt;p&gt;26 points; 3 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/2509.16506/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.CV</category></item><item><title>CommonForms: A Large, Diverse Dataset for Form Field Detection — mentioned by jbarrow</title><link>https://news.ycombinator.com/item?id=45566892</link><guid isPermaLink="false">urn:hackerpapers:mention:2509.16506:45566892</guid><pubDate>Mon, 13 Oct 2025 10:40:27 GMT</pubDate><description>&lt;p&gt;Training ML models for PDF forms. You can try out what I’ve got so far with this service that automatically detects where fields should go and makes PDFs fillable: https://detect.semanticdocs.org/ Code and models are at: https://github.com/jbarrow/commonforms That’s built on a dataset and paper I wrote called CommonForms, where I scraped CommonCrawl for hundreds of thousands of fillable form pages and used that as a training set: https://arxiv.org/abs/2509.16506 Next step is training and releasing some DETRs, which I think will drive quality even higher. But the ultimate end goal is working…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2509.16506/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CV</category></item><item><title>CommonForms: A Large, Diverse Dataset for Form Field Detection — mentioned by jbarrow</title><link>https://news.ycombinator.com/item?id=45450138</link><guid isPermaLink="false">urn:hackerpapers:mention:2509.16506:45450138</guid><pubDate>Thu, 02 Oct 2025 14:31:58 GMT</pubDate><description>&lt;p&gt;Existing “auto-fillable” tools are pretty lackluster in my experience. CommonForms is tooling that can automatically detect form fields in PDFs and turn those PDFs into fillable documents. The dataset is ~500k form pages pulled from Common Crawl, which I trained the object detectors on. For being vision only, the results are pretty remarkable! Releasing the dataset, paper, models, and (imo most importantly) simple/convenient tooling to automatically prepare any PDF. Links: Repo: https://github.com/jbarrow/commonforms - Paper: https://arxiv.org/abs/2509.16506&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2509.16506/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CV</category></item></channel></rss>
