Scanning and data capture are usually bought together and fail separately. Scanning produces an image, and its failures are about input: resolution, orientation, lighting and consistency. Capture reads that image, and its failures are about uncertainty: what happens to a field the software is not sure about. Diagnosing which one is hurting you saves buying the wrong fix.
Scanning failures and their fixes
Skewed pages, poor contrast, photographs taken at an angle, inconsistent resolution between people scanning on their own devices. The fixes are organisational: a short written standard, scanning at arrival rather than at processing, and a capture app that rejects a poor image at the point of capture rather than accepting anything.
Capture failures and their fixes
Confident wrong values with no signal that the software was unsure, and no review queue to catch them. The fix is confidence thresholds per field and a review screen showing the document beside the extracted value. A product that returns values with no confidence signal leaves you choosing between checking everything and checking nothing.
Diagnosing which you have
Look at corrections for a month. If the errors cluster on poor images, it is a scanning problem and no engine change will fix it. If they cluster on line items and inferred fields across good images, it is a capture problem, and the answer is threshold tuning and a better review path rather than better hardware.
Questions people ask about invoice scanning and data capture
Which should we address first?
Whichever the corrections point at. Input quality is usually cheaper to fix and is often mistaken for a software problem.
Is a phone camera acceptable?
For carefully taken photographs of text-layer documents, usually. For creased thermal receipts in poor light, expect review.
Should we standardise scanning settings?
Yes, and check occasionally. Consistent input beats a better engine fed a mixture, reliably.