The AI kept the right words. Why does my product label still look wrong?

Build a small label map

Record the real brand, product name, size and any important edition identifier. Mark their order and physical location on the package. For a fictional serum bottle, the same four phrases placed on the cap instead of the front label would still depict the wrong product. A text extraction that finds all four phrases cannot resolve that positional failure.

Pebblely describes extracting text and position information to guide reference-led generation. That is a useful mechanism to study, not proof that any tool reproduces every label correctly. Keep the actual package reference as the authority.

Proof four independent properties

OCR can assist with finding a word error, but it is not a complete layout verifier. Also check whether the product curvature, reflections or occlusion make an identifying line unreadable at the actual delivered size.

Separate annotations from package printing

Added selling-point text should remain clearly outside the physical label unless it truly belongs to the product. Do not let an extra callout become simulated manufacturer artwork. If the generated label structure is wrong despite a sharp source, reject that candidate or use appropriate external artwork correction. A new elaborate prompt is less useful than a clearly identified positional defect.

Record a precise acceptance

For your approved image, retain a reference crop and a final crop with the reviewed regions marked in your private record. State which lines and positions were checked and which small text remains unresolved. Itemlume does not automatically certify OCR or label layout. An output can pass an exact-word check while failing product identity, so keep the release decision broader than “OCR passed.”