<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>LayoutParser on SoloSoft</title><link>https://www.solosoft.dev/tags/layoutparser/</link><description>Recent content in LayoutParser on SoloSoft</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Fri, 01 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://www.solosoft.dev/tags/layoutparser/index.xml" rel="self" type="application/rss+xml"/><item><title>LayoutParser: Unified Open-Source Toolkit for Document Image Analysis</title><link>https://www.solosoft.dev/post/layout-parser-document-ai-2026/</link><pubDate>Fri, 01 May 2026 00:00:00 +0000</pubDate><guid>https://www.solosoft.dev/post/layout-parser-document-ai-2026/</guid><description>&lt;p&gt;If you have ever tried to extract structured information from a scanned PDF, a historical newspaper archive, or a stack of invoices, you know the pain: every document looks different, every model expects a different input format, and every OCR engine spits out text in a different coordinate system. &lt;strong&gt;LayoutParser&lt;/strong&gt; was built to end that chaos.&lt;/p&gt;
&lt;p&gt;Developed by the &lt;a href="https://github.com/Layout-Parser/layout-parser"&gt;Layout-Parser team&lt;/a&gt;, this open-source deep learning toolkit provides a &lt;strong&gt;unified interface&lt;/strong&gt; for document image analysis tasks including layout detection, OCR integration, and visual information extraction. With over &lt;strong&gt;4,000 GitHub stars&lt;/strong&gt;, LayoutParser has become the go-to library for researchers and practitioners who need to turn document images into structured, machine-readable data.&lt;/p&gt;</description></item></channel></rss>