Characters and Codes (OCR/OCV, Barcode, 2D Codes) FAQs

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Frequently Asked Questions

  • OCR reads what the characters are.
  • OCV verifies the characters match expected content and meet print-quality expectations.

OCV is often used when quality systems require confidence that codes are not only readable, but correct and well formed.

Yes, provided the system is designed around:

  • The mark type (etched vs printed vs embossed)
  • Proper lighting (often angled/dark-field for etch marks)
  • Sufficient resolution and focus
  • Code localization and verification rules

Poor contrast or surface glare is usually solved with lighting and optics changes.

A typical workflow includes:

  • Locate the symbol region
  • Attempt decode
  • Validate content (format, expected prefixes, check digits)
  • Optionally grade symbol quality (especially for compliance needs)

2D codes are typically more robust for small footprints and damaged symbols, but still rely on print/mark quality.

Key factors include:

  • Contrast and edge sharpness
  • Quiet zone spacing
  • Curvature or distortion on bottles/tubes
  • Motion blur at speed
  • Glare from reflective packaging or films

Better lighting and stable capture conditions usually fix “intermittent read” issues.

Common approaches include:

  • Diffuse lighting to reduce specular reflections
  • Polarizing filters
  • Coaxial lighting for flat reflective surfaces
  • Camera angle adjustments so glare moves off the code region

Glare control is often more important than higher camera resolution.

Successful OCR on low contrast typically uses:

  • Coaxial or diffuse lighting for even illumination
  • Polarization to reduce glare
  • Backlighting for certain translucent substrates
  • Increased resolution/focus stability

For highly inconsistent printing, AI-driven OCR can boost robustness.

Yes—common steps are:

  • Read text with OCR
  • Validate date format and expected pattern rules
  • Cross-check against production batch parameters
  • Log results (pass/fail + string read + image)

This supports traceability and helps prevent mislabeling incidents.

Best practices include:

  • Constrain print location and font where possible
  • Use pattern-based OCR regions (anchored to stable features)
  • Train OCR profiles for known fonts/variations
  • Use AI OCR for high variability cases
  • Implement confidence thresholds and exception handling

A well-defined changeover/recipe strategy is often the difference-maker.

Yes—typical reject logic includes:

  • Missing code region
  • Decode failure or low confidence
  • Code content mismatch vs recipe rules
  • Code quality below threshold

Most systems also store failure images and the attempted read string for troubleshooting.