OpenBMB Releases MiniCPM-OCR 1.0 and Verification Model Jev
OpenBMB releases MiniCPM-OCR 1.0, a 1B-scale OCR model that achieves 93.68 on Real5-OmniDocBench and 54.7 on OCRBench v2. It also releases MiniCPM-OCR Jev, a 2B decision model used to compare parsing results with the original image and give a 0–1 correctness score, approaching Qwen3.5-397B-A17B on JevOCRBench. Both models have open weights, support deployment via llama.cpp, vLLM, SGLang, and Ollama, and come with open-source fine-tuning code.
Why read this
AI score 60
OpenBMB has open-sourced a 1B OCR model and a 2B verification model, along with deployment methods for local on-device running and multiple inference frameworks.
Topics
Body
🎬 MiniCPM-OCR in action — running locally on iPhone Duo and iPad. Parse fast and accurately. Guard the results. All in your hands. 🙌
Handwritten notes, complex formulas, newspapers, poor-quality scans—real documents put OCR to the test.
📄 MiniCPM-OCR 1.0 — SOTA OCR performance and speed at 1B scale. It achieves 93.68 on Real5-OmniDocBench and 54.7 on OCRBench v2, combining accurate document parsing with efficient inference.
🛡️ MiniCPM-OCR Jev — A 2B decision model that checks OCR accuracy. It approaches Qwen3.5-397B-A17B on JevOCRBench and works with outputs from different OCR parsers.
Watch it compare parsed text, formulas, and tables with the original image and assign 0–1 correctness scores—helping you pick better results and identify what needs human review.
🔓 Open weights. Deploy your way. Run with llama.cpp, vLLM, SGLang, or Ollama. Customize with open-source fine-tuning code.
Watch it in action. 👇
🔗 GitHub: https://t.co/2SlATAJk08 🤗 Hugging Face: https://t.co/Ttq31GL8eJ 👉 Try the Demo：https://t.co/QsFAJT3AFy Use the API：https://t.co/dP01jBS2j2