Help prevent product-to-label mix-ups by comparing visible label features with the selected production recipe.
A pack can carry a label and still be wrong. Verification tools look for distinctive features—such as artwork regions, text patterns, symbols, codes or colour areas—that differentiate one approved label from another.
Identify label variants using features that remain visible and reliable under normal production conditions. The system can compare the read result with a product selection, upstream control or active line recipe.
The recipe-selection and line-clearance process must be designed so the correct reference is active for the run.
Inspect critical logos, regulatory symbols, layout features or text blocks against an approved reference. This is not a substitute for artwork approval; it verifies the visible production label against the configured master.
Define critical-to-quality regions and realistic tolerances instead of trying to compare every printed pixel.
Check that a label or selected colour region falls within an agreed range. Lighting, material finish, print variation and camera calibration determine what can be differentiated reliably.
Colour checks are proved using approved good variation and known wrong-label samples.
The vision system captures a controlled image of each label and checks one or more stable identity features—such as a logo, artwork region, product name, barcode value, printed code or colour block. That result is compared with the active line recipe or upstream product selection. A mismatch can generate an automatic reject, line stop, alarm and result record.
A robust project separates fixed features from information that changes by batch. This helps avoid loose tolerances while keeping normal print and material variation acceptable.
Compare a visible label signature, product name or decoded GTIN with the active recipe.
Check agreed logos, warnings, regulatory symbols and layout regions against the approved master.
Read or verify dates, batch information and serial fields using OCR or OCV where suitable.
Decode barcodes, QR codes and Data Matrix symbols and compare their content with the expected value.
We build a challenge set containing approved variants, known wrong labels and expected production variation. The agreed challenge tests become the basis for acceptance and operator procedures.
