# I just parsed 500 research papers in under 10 minutes. On CPU. No GPU. No clo...
Canonical: https://social-archive.org/philippschaefer/SHlVJrGW87
Original URL: https://www.linkedin.com/feed/update/urn:li:activity:7456334529499373568/
Author: Hao Hoang
Platform: linkedin
## Content
I just parsed 500 research papers in under 10 minutes. On CPU. No GPU. No cloud. 𝐎𝐩𝐞𝐧𝐃𝐚𝐭𝐚𝐋𝐨𝐚𝐝𝐞𝐫 𝐏𝐃𝐅, the open-source PDF parser that just hit #1 on every benchmark that matters for AI. Here's what blew me away: 1️⃣ 0.907 overall accuracy (beats docling, marker, pymupdf4llm) 2️⃣ 0.928 table extraction, the hardest thing to get right in PDFs 3️⃣ 60+ pages/second in local mode. 100+ pages/sec with batch on 8-core machines 4️⃣ Bounding boxes on every element, exact pixel coordinates for RAG citations 5️⃣ Built-in prompt injection filtering (most parsers don't even think about this) And it's 3 lines of Python: 𝘱𝘪𝘱 𝘪𝘯𝘴𝘵𝘢𝘭𝘭 𝘰𝘱𝘦𝘯𝘥𝘢𝘵𝘢𝘭𝘰𝘢𝘥𝘦𝘳-𝘱𝘥𝘧 𝘰𝘱𝘦𝘯𝘥𝘢𝘵𝘢𝘭𝘰𝘢𝘥𝘦𝘳_𝘱𝘥𝘧.𝘤𝘰𝘯𝘷𝘦𝘳𝘵( 𝘪𝘯𝘱𝘶𝘵_𝘱𝘢𝘵𝘩=["𝘧𝘰𝘭𝘥𝘦𝘳/"], 𝘰𝘶𝘵𝘱𝘶𝘵_𝘥𝘪𝘳="𝘰𝘶𝘵𝘱𝘶𝘵/", 𝘧𝘰𝘳𝘮𝘢𝘵="𝘮𝘢𝘳𝘬𝘥𝘰𝘸𝘯,𝘫𝘴𝘰𝘯" ) For complex tables and scanned PDFs, hybrid mode routes pages to an AI backend, still running 100% locally. Also: it's the first open-source tool to auto-generate Tagged PDFs for accessibility compliance (EAA deadline was June 2025, a lot of teams are scrambling). 19.9k stars. Apache 2.0. Worth 30 seconds of your time. What PDF parser are you using for your RAG pipeline right now? 👇