📄 Can your document agent show why a result should be trusted and flag what needs another look?

Community developer @mucerx built TraceCell, a local-first document parsing and verification agent powered by MiniCPM. 🔍

Three models work together:
📖 MiniCPM-OCR 1.0 parses document content.
⚖️ MiniCPM-OCR Jev scores recognition candidates.
🧠 MiniCPM-V 4.7 reviews ambiguous regions, with its answers scored again before acceptance.

📍 Every field links back to source-image coordinates, confidence, and review status in an evidence ledger.

🔄 Uncertain results trigger targeted re-parsing, model review, or human escalation. Documents are routed for automatic acceptance, spot checks, or full review.

🧮 TraceCell also checks arithmetic and reconciles documents—catching mismatches even when the text itself was recognized correctly.

🧪 The author’s current demo covers 5 synthetic documents, 83 regions, and 52 business fields. Calibration on 78 labelled samples reports 80.77% automatic acceptance with no incorrect acceptances. These are sample-set results, not independently verified production guarantees.

With MCP and Skill support, it’s a practical community example of turning visual understanding into traceable decisions. 💙

🛠️ Explore TraceCell:
github.com/logicore-code/Trac…

🚀 Build with MiniCPM:
GitHub — MiniCPM-OCR:
github.com/OpenBMB/MiniCPM-OCR
GitHub — MiniCPM-V:
github.com/OpenBMB/MiniCPM-V
🤗 Hugging Face — MiniCPM-OCR 1.0:
huggingface.co/openbmb/MiniCP…

#MiniCPM #OpenBMB #DocumentAI #OpenSource