MiniCPM-OCR Jev 2B model scores OCR results with 93.68% accuracy on real-world benchmarks
MiniCPM-OCR Jev (2B), a 2-billion-parameter model, achieves 93.68% AUROC on real-world document parsing, outperforming larger models like JEV-35B-A3B (90.1%) and nearly matching Qwen3.5-397B-A17B (93.8%) on the JevOCRBench dataset. Developed by the same team behind MiniCPM-OCR 1.0, it evaluates OCR outputs for text, formulas, and tables by scoring candidate accuracy on a 0–1 scale, enabling…
Key points
- MiniCPM-OCR Jev 2B scores OCR results with **93.68% AUROC** on JevOCRBench, surpassing larger models like JEV-35B-A3B (90.1%)
- Model evaluates text, formulas, and tables by comparing candidate text to region images, enabling automated filtering or human review
- Deployment requires threshold tuning based on error budgets and document types; built-in rendering or manual pre-processing is optional
A 1.3B OCR model just hit 93.68 on real-world document parsing and there's now an AI judge for OCR errors
huggingface.co · 27 October 2026
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