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.