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| title: Document Verification Lab |
| emoji: 🔎 |
| colorFrom: green |
| colorTo: gray |
| sdk: static |
| app_file: index.html |
| pinned: false |
| license: mit |
| short_description: Explore OCR evidence and visual verification tradeoffs |
| --- |
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| # Document Verification Lab |
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| An interactive replay of real MiniCPM-OCR and OCR Jev inference on 120 generated invoice regions, with 48 separate stress candidates. By Guilin Zhang. |
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| Explore source pixels, extracted text, rendered candidates and raw scores. Adjust the review threshold and compare with simple rules. GPU calls were recorded locally; this static Space does not run live inference. |
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| [Watch the full USD workflow · 1:45](https://huggingface.co/spaces/guilindev/document-verification-lab/resolve/main/walkthrough.mp4): upload, OCR, visual comparison, classification head, score-based review, correction, and downstream invoice checks. The default case is DVL-0090, with a $4,271.98 source total and a deliberately altered $42,719.80 candidate. |
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| [Code, methodology, source data and model revisions](https://github.com/GuilinDev/document-verification-lab) |
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| Main result: on 80 held-out regions, rules caught all three natural errors. Adding the verifier increased review from 3 to 16 cases, including 13 correct items. On 24 separately injected, internally consistent currency/amount errors, the verifier caught all 24 that rules missed. No fine-tuning was performed. |
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| Models: [MiniCPM-OCR 1.0](https://huggingface.co/openbmb/MiniCPM-OCR-1.0) and [MiniCPM-OCR Jev](https://huggingface.co/openbmb/MiniCPM-OCR-Jev), OpenBMB, Apache-2.0. Project code and generated data: MIT. Video music licensed for synchronized use; standalone track excluded. |
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