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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>DeepOtsu: Document Enhancement and Binarization using Iterative Deep Learning · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1901.06081/</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/1901.06081/feed.xml" rel="self" type="application/rss+xml" /><item><title>DeepOtsu: Document Enhancement and Binarization using Iterative Deep Learning — mentioned by MassPikeMike</title><link>https://news.ycombinator.com/item?id=45818361</link><guid isPermaLink="false">urn:hackerpapers:mention:1901.06081:45818361</guid><pubDate>Wed, 05 Nov 2025 02:39:00 GMT</pubDate><description>&lt;p&gt;I am usually working with historical documents, where both Otsu and adaptive thresholding are frustratingly almost but not quite good enough. My go-to approach lately is &amp;quot;DeepOtsu&amp;quot; [1]. I like that it combines the best of both the traditional and deep learning worlds: a deep neural net enhances the image such that Otsu thresholding is likely to work well. [1] https://arxiv.org/abs/1901.06081&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1901.06081/"&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>
