I compared even more parsers on 14 PDF-parsing capabilities using different type

<!-- SC_OFF --><div class="md"><p>Hey everyone,</p> <p>I’ve built a fairly flexible RAG pipeline that was initially designed to handle any type of document (PDFs, reports, mixed

<!-- SC_OFF --><div class="md"><p>So far I have only tried Qwen and olmOCR. My biggest struggle at the moment has been extracting a date that is oriented in a title block, where

<!-- SC_OFF --><div class="md"><p>Hey everyone,</p> <p>I’m pretty new to the AI engineering side of things, but I've recently been tasked with a massive digitization project at

<!-- SC_OFF --><div class="md"><blockquote> <p><strong>WARNING: wait before download from HF: I just realised my upload of the new versions with the additional fix in the chat

<!-- SC_OFF --><div class="md"><p>We just released ParseBench, a benchmark designed to evaluate how well document parsers and OCR systems actually work when feeding data into AI

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1v479m5/paddlepaddlehpdparsing_hugging_face/"> <img alt="PaddlePaddle/HPD-Parsing · Hugging Face"

<!-- SC_OFF --><div class="md"><p>So in response to the Great Token Reconning of 2026, I decided to try out Qwen 3.6 as a daily driver, and although it's only been about a day, I

<!-- SC_OFF --><div class="md"><p>Qwen 3.8 27b </p> <p>Llama.cpp pp 544 tg 40 Exllama pp 633 tg 95</p> <p>Qwen 3.6 35b a3b</p> <p>Llama.cpp pp 563 tg 84 Exllama pp 2274 tg 150</p>