[GitHub](https://github.com/OpenBMB/MiniCPM-OCR) · [Free API](https://platform.modelbest.ai/console/docs/api/ocr-jev) · [Usage](https://github.com/OpenBMB/MiniCPM-OCR#using-minicpm-ocr) · [Skill](https://github.com/OpenBMB/MiniCPM-OCR#skill)
**MiniCPM-OCR Jev** is a **2B OCR correctness decision model** for text, formula, and table
regions. Given a region image and a candidate OCR result, it returns a **score from 0 to 1** —
higher means the candidate is more likely correct. Use it to auto-accept reliable regions,
route the rest to human review, or pick the best result among several OCR models.
- 🛡️ **A small model that beats larger ones**: at only 2B parameters, decision AUROC (×100) on
[JevOCRBench](https://huggingface.co/datasets/openbmb/JevOCRBench) is **93.6**, above
JEV-35B-A3B (90.1) and close to Qwen3.5-397B-A17B (93.8).
- 🧩 **Stable generalization**: on parses from MiniCPM-OCR 1.0, GLM-OCR, PaddleOCR-VL-1.6, and
Xiaomi-OCR-0, AUROC (×100) stays in **91.9–93.4**.
### Architecture
Trained from **Qwen3.5-2B**. The vision encoder and language backbone are retained, and the
language-model output head `lm_head` is replaced with a **binary classification head** that
learns whether a candidate is correct.
#### Inputs and outputs
| Item | Description |
| :--- | :--- |
| Assessment unit | One text / formula / table region plus its candidate OCR result |
| Original region image | The crop from the document |
| Candidate content | OCR text and an image rendered from that text |
| Output | Score in 0–1; higher = more likely correct |
Internally, the model uses a matched original region image, candidate text, and rendered
image. When calling the cloud API, the Docker image, or the SDK service with a built-in
renderer, provide the original region image, candidate text, and the correct region category
(text, formula, or table); the service handles rendering. The SDK service without a renderer
requires the caller to supply the rendered image. See the [deployment guide](https://github.com/OpenBMB/MiniCPM-OCR/blob/main/docs/minicpm_ocr_jev_tutorial_en.md) for request fields
and image encoding formats.
### Evaluation Results
The table below reports overall and per-category AUROC (×100) on JevOCRBench. Higher values
indicate better discrimination between correct and incorrect parsing results.
