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An invoice can pass every arithmetic check

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and still contain the wrong amounts.

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Let me show you.

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I upload a dollar invoice.

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MiniCPM-OCR reads its payment region into editable fields,

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keeping the source beside them.

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Now I deliberately multiply every amount by ten.

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Subtotal, shipping and tax still add up.

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The rules pass,

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although the original total is clearly different.

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OCR Jev checks this against the page.

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It receives the source image, the candidate text,

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and an image rendered from that text.

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That rendered copy makes the claimed content

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visible for comparison.

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Inside the model, the inputs become a shared representation.

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A small classification head scores two outcomes:

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consistent and inconsistent.

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It returns a consistency score directly.

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Higher scores mean stronger agreement with the source.

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The correct candidate scores nearly 0.99.

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Our altered candidate scores just 0.004.

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We compare that score with a threshold

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chosen on separate calibration examples.

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This invoice enters the review queue.

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The reviewer opens its source,

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restores the correct amounts,

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and confirms the correction.

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For accounts payable, the corrected fields can continue

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to purchase order matching and approval,

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with their evidence attached.

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I also tested whether this extra model actually helps.

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On eighty held out synthetic invoices,

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rules caught all three natural errors.

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The verifier added thirteen reviews

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and caught no extra errors.

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In a separate stress test,

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it flagged all twenty four deliberately altered candidates

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that passed the rules.

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The useful measure is errors caught per unit of review work.

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The online lab lets you inspect every case

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and explore that tradeoff yourself.