The latest articles on Forem by Adam LB (@adam_lb_ab2b034962edcc005). https://forem.com/adam_lb_ab2b034962edcc005 https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F3506386%2F88dd6ebd-bf22-478a-bafb-b9eecd8a5568.png https://forem.com/adam_lb_ab2b034962edcc005 en Adam LB Tue, 16 Sep 2025 11:25:49 +0000 https://forem.com/adam_lb_ab2b034962edcc005/exploring-document-parsing-with-different-ai-models-3gnk https://forem.com/adam_lb_ab2b034962edcc005/exploring-document-parsing-with-different-ai-models-3gnk <p>I’m excited to share a new project I’ve been working on: <a href="https://github.com/AdemBoukhris457/Documents-Parsing-Lab" rel="noopener noreferrer"><strong>Documents-Parsing-Lab</strong></a></p> <p>This project is not a benchmark, but an <strong>exploration lab</strong> — built around a collection of Jupyter notebooks that test different AI models on various document structures. The idea is simple: <em>let users see the parsing results directly, compare approaches, and understand where each model shines (and where it struggles).</em></p> <h2> 🔍 What’s Inside? </h2> <p>Each notebook applies one or more models to different real-world document types, including:</p> <ul> <li><strong>PDFs</strong></li> <li><strong>Tables</strong></li> <li><strong>Charts &amp; Figures</strong></li> <li><strong>Complex layouts</strong></li> </ul> <h2> ⚡ Models Explored So Far </h2> <ul> <li> <p><strong>OCR &amp; Vision Models</strong></p> <ul> <li>Dolphin (ByteDance)</li> <li>Typhoon_OCR_7B</li> <li>MonkeyOCR</li> <li>PaddleOCR</li> </ul> </li> <li> <p><strong>Table Structure Recognition</strong></p> <ul> <li>UniTable</li> <li>TableFormer</li> <li>Microsoft Table Transformer</li> </ul> </li> </ul> <p>(…and more to come!)</p> <h2> ✨ Why This Project? </h2> <p>There are lots of benchmarks out there — but in practice, people want to <em>see</em> how models handle real documents. This lab is meant to be a <strong>hands-on exploration space</strong> where you can:</p> <ul> <li>Run the notebooks yourself</li> <li>Compare outputs across models</li> <li>Understand limitations before using them in production</li> </ul> <h2> 📈 What’s Next? </h2> <p>More models and examples will be added over time. The goal is to keep expanding this into a growing resource for anyone interested in:</p> <ul> <li>OCR</li> <li>Document AI</li> <li>Structured data extraction</li> </ul> webdev ai computerscience machinelearning