What must the code prove?
Presence only, readable print, the correct date, the correct batch or lot, an approved format, product-to-code agreement—or a combination.
Date code inspection buyer guide
How to specify a date code inspection system
Define what the printed code must prove, how the product will be presented and what the line must do when a check fails.
Start with the production risk01 02 03 04 05 06 07
Seven decisions determine whether the inspection will be reliable
Where does the expected value come from?
A fixed recipe, shelf-life calculation, operator entry, PLC, printer, MES, database or job-management system.
Can every product be presented consistently?
Define orientation, spacing, conveyor speed, movement, curvature and the permitted variation in code position.
Which optics reveal the print?
Camera resolution, lens, field of view, exposure and lighting geometry must suit the smallest character and substrate.
How should the characters be assessed?
Choose presence, pattern, OCR, OCV or a trialled AI-assisted method according to how much the content and appearance can vary.
What happens after a failure?
Alarm, stop, track and reject, confirm removal, monitor the reject bin or escalate repeated failures.
Which evidence must be retained?
Counters, read strings, timestamps, failure classifications, selected images and production-system events.
Inspection method
OCR, OCV, pattern tools or AI-assisted vision?
The best method is the simplest approach that remains stable across genuine good production and rejects every unacceptable fault.
| Method | Best suited to | Typical output | Key consideration |
|---|---|---|---|
| Presence / pattern | Proving a code or known feature exists | Pass or fail | Does not necessarily identify every character |
| OCR | Reading changing or previously unknown strings | Captured text plus confidence | Font, print quality and segmentation affect reliability |
| OCV | Confirming a known expected string | Match or mismatch by character | The expected value must be supplied correctly |
| AI-assisted vision | Applications with broad, representative good-print variation | Classification, read or anomaly result | Requires suitable training and validation samples |
AI is not a substitute for clear acceptance criteria. It should be selected only when trials show it improves robustness for the real application.
System sequence
How inline date code verification works
Reliable inspection treats product handling, image capture, code logic and reject control as one timed process.
Present
Guide and space the product so the complete code enters a repeatable field of view.
Capture
Trigger the camera and lighting to freeze the printed field at production speed.
Inspect
Apply presence, print-quality, OCR, OCV, format and data-comparison rules.
Act
Pass, reject or stop, then record the result required by the production risk.
Application checklist
Information that shortens the route to a reliable trial
- Good packs and deliberately poor code samples
- All code formats, prefixes and permitted separators
- Smallest character size and maximum code area
- Line speed, product pitch and conveyor dimensions
- Printer make, print technology and substrate
- Expected-data source and recipe-change process
- Available reject distance and preferred reject method
- Required counters, images and data exchange
Plain-English glossary
- OCR
- Reads characters and returns the observed string.
- OCV
- Verifies printed characters against a known value.
- No-read
- No sufficiently reliable string could be obtained.
- Wrong-read
- The observed string differs from the expected value.
- Region of interest
- The defined image area in which the code should appear.
- Reject confirmation
- Independent evidence that the failed product was removed.
System routes
Choose the recognition and data check separately
The camera first has to see and interpret the mark. The control logic then has to decide whether that observed value is authorised for the current product.
| Requirement | Typical route | Planning page |
|---|---|---|
| Known printed value | OCV or pattern comparison | OCV verification |
| Changing human-readable text | Conventional or AI-assisted OCR plus expected-data checks | OCR / AI OCR |
| MFG or production-date logic | OCR plus format, calendar and shelf-life rules | Manufacturing-date inspection |
| Encoded product and traceability data | 1D or 2D reading plus field validation | Barcode & Data Matrix |
| Correct packaging and variable print | Label identity plus date, batch and recipe agreement | Label & date verification |
| Hardware architecture | Smart camera, fixed reader, PC-based or multi-camera station | Camera & reader systems |
Frequently asked questions
Date code inspection system questions
Use the answers as a starting point, then prove the final method with representative products and print.
What information is needed to specify a date code inspection system?
Representative good and bad packs, code examples, permitted formats, line speed, product spacing, conveyor dimensions, printer details, changeover rules, reject requirements and the source of the expected data are the most useful starting points.
Should a date code use OCR or OCV?
Use OCR when the system must read a changing or previously unknown string. Use OCV when the expected value is already known and the task is to confirm that the printed characters match it. Some applications combine both.
Is AI vision always better than OCR?
No. Controlled codes often achieve a clearer and more explainable result with OCR, OCV or pattern tools. AI-assisted vision is considered when genuine good-print variation is too broad for a stable rule-based method.
Can inspection results be stored for audits?
Yes. Depending on the required scope, the system can retain counters, timestamps, failure reasons, read strings and selected images. Storage duration and data exchange should be defined during specification.
Useful official background: GOV.UK food information guidance and mandatory food-label information.
Engineering review
Turn the checklist into an inspection trial
Share the packs, code formats, line drawing and required failure response. We will identify the practical next step.
OCR vs OCV: what is the difference?
OCV verifies printed characters against a known expected value; OCR reads characters into data where the value may vary. The right method depends on whether the expected message is controlled, how variable the code is and what evidence the production record must retain.