Supercharge your OCR Pipelines with Open Models

Back to Articles Supercharge your OCR Pipelines with Open Models Published October 21, 2025 Update on GitHub Upvote 301 +295 merve merve Follow Aritra Roy Gosthipaty ariG23498 Follow Daniel van Strien davanstrien Follow Hynek Kydlicek hynky Follow Andres Marafioti andito Follow Vaibhav Srivastav reach-vb Follow Pedro Cuenca pcuenq Follow We have added Chandra and OlmOCR-2 to this blog, as well as OlmOCR Scores of the models 🫡 TL;DR: The rise of powerful vision-language models has transformed document AI. Each model comes with unique strengths, making it tricky to choose the right one. Open-weight models offer better cost efficiency and privacy. To help you get started with them, we’ve put together this guide. In this guide, you’ll learn: The landscape of current models and their capabilities When to fine-tune models vs. use models out-of-the-box Key factors to consider when selecting a model for your use case How to move beyond OCR with multimodal retrieval and document QA By the end, you’ll know how to choose the right OCR model, start building with it, and gain deeper insights into document AI. Let’s go! Table-of-Contents Supercharge your OCR Pipelines with Open Models Brief Introduction to Modern OCR Model Capabilities Transcription Handling complex components in documents Output formats Locality Awareness in OCR Model Prompting Cutting-edge Open OCR Models Comparing Latest Models Evaluating Models Benchmarks Cost-efficiency Open OCR Datasets Tools to Run Models Locally...