OpenBMB 转发并确认了 MiniCPM-OCR 与 MiniCPM-OCR Jev 的组合用法：前者以 1.3B 参数解析中英文扫描件、手机照片、倾斜页面和关键信息，后者对提取出的文本、公式和表格给出可靠性评分。Jev 只标记可能不可靠的输出，不自动纠正，也可用于校验其他 OCR 模型的输出；项目提供开放权重、微调支持，并兼容 llama.cpp、vLLM、SGLang 和 Ollama。
引用IMOLEAYOMIDE@Olaemmy1200
OCR pipelines can be much more reliable when you add a verification model.
Instead of trusting every extracted result, you can use a second model to check how accurate the output actually is.
A good example is MiniCPM OCR from @OpenBMB and ModelBest.
It combines a lightweight document parser with a separate model, MiniCPM OCR Jev, designed to evaluate OCR reliability.
Here’s how it works:
1. Document parsing
MiniCPM-OCR uses just 1.3B parameters to extract information from Chinese and English documents.
It handles scanned documents, phone photos, curved pages, tilted inputs, and even key information extraction.
2. Output verification
MiniCPM-OCR Jev evaluates the extracted text, formulas, and tables to estimate how reliable the results are.
You can also use it to verify outputs from other OCR models, meaning you don’t have to replace your existing pipeline.
Why does this matter for AI agents?
Agents are increasingly working with PDFs, invoices, research papers, and other structured documents.
But one incorrect number or poorly extracted table can affect every step that follows.
A simple workflow would look like this:
→ Parse the document with MiniCPM-OCR.
→ Pass the extracted content to Jev for reliability scoring.
→ Route low-confidence results to a human reviewer or another correction step.
One important distinction: Jev identifies potentially unreliable outputs. It doesn’t automatically correct them.
The project also provides open weights, fine tuning support, and inference compatibility with llama.cpp, vLLM, SGLang, and Ollama.
There are also integrations through Agent Skills.
If you’re building document processing agents, this is worth testing.
Especially on difficult inputs like dense tables, mathematical formulas, scanned pages, and low quality phone photos.
The bigger idea: Don’t just build agents that extract information. Build pipelines that can also evaluate whether that information should be trusted.
