<rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Hacker News: phenomen</title><link>https://news.ycombinator.com/user?id=phenomen</link><description>Hacker News RSS</description><docs>https://hnrss.org/</docs><generator>hnrss v2.1.1</generator><lastBuildDate>Tue, 25 Aug 2026 00:22:19 +0000</lastBuildDate><atom:link href="https://hnrss.org/user?id=phenomen" rel="self" type="application/rss+xml"></atom:link><item><title><![CDATA[New comment by phenomen in "OCR It – pull text out of un-copyable documents for your LLM"]]></title><description><![CDATA[
<p>I tested many open-source and hosted OCR models and Datalab Chandra was the most accurate. It can parse complex layouts, tables, handwriting, and formulas at a fraction of the cost of Claude/Gemini.<p>Local: <a href="https://github.com/datalab-to/chandra" rel="nofollow">https://github.com/datalab-to/chandra</a>
Hosted: <a href="https://www.datalab.to" rel="nofollow">https://www.datalab.to</a><p>Another decent option is GLM OCR. It's slightly less accurate but faster and cheaper.<p>Local: <a href="https://github.com/zai-org/GLM-OCR" rel="nofollow">https://github.com/zai-org/GLM-OCR</a>
Hosted: <a href="https://docs.z.ai/guides/vlm/glm-ocr" rel="nofollow">https://docs.z.ai/guides/vlm/glm-ocr</a><p>Other models such as PaddleOCR, dots.ocr and DeepSeek OCR performed significantly worse.</p>
]]></description><pubDate>Mon, 24 Aug 2026 15:23:40 +0000</pubDate><link>https://news.ycombinator.com/item?id=49421088</link><dc:creator>phenomen</dc:creator><comments>https://news.ycombinator.com/item?id=49421088</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=49421088</guid></item></channel></rss>