---
title: "How to check print quality with computer vision"
description: "Mount a camera over the press output, compare each sheet or section of the web against the approved proof, and use a model trained on the shop own images to mark likely streaks, spots, smears, missing ink, registration shifts and color that drifts from the proof. Each mark points to the sheet or roll position, so the press operator can check it and adjust the press, and the job lead can decide what to pull. The model only flags sheets worth a look; it does not replace the approved proof, color sign-off or customer approval, and people decide what ships."
url: https://lidvizion.ai/answers/how-to-check-print-quality-with-computer-vision/
site: Lid Vizion
index: https://lidvizion.ai/llms.txt
---

[Direct answers](https://lidvizion.ai/answers/)

# How can a print shop or packaging printer use computer vision to catch print defects on the press?

Mount a camera over the press output, compare each sheet or section of the web against the approved proof, and use a model trained on the shop own images to mark likely streaks, spots, smears, missing ink, registration shifts and color that drifts from the proof. Each mark points to the sheet or roll position, so the press operator can check it and adjust the press, and the job lead can decide what to pull. The model only flags sheets worth a look; it does not replace the approved proof, color sign-off or customer approval, and people decide what ships.

## What this means in practice

- Mount a fixed camera with steady lighting where printed sheets or the web leave the press
- Load the approved proof for each job so the tool has a reference to compare against
- Train the model on the shop own images of good and defective prints for each substrate and ink set
- Alert the press operator to flagged sheets and log flags by job and roll position
- Add reviewed images back into training whenever a new substrate, ink or press comes in

## How Lid Vizion approaches it

We start with the business decision and operating context, define ownership and measurable milestones, then use configuration, integrations, automation, or custom software only where each earns its place.

## More on Automation & custom AI

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- [How can a quarry, plant or yard use drone photos and computer vision to track stockpiles of sand, gravel, wood chips or other bulk material?](https://lidvizion.ai/answers/how-to-estimate-stockpile-volumes-from-drone-photos-with-computer-vision/)
- [How can a pallet recycler or warehouse use computer vision to sort returned pallets into reuse, repair or scrap?](https://lidvizion.ai/answers/how-to-grade-used-wooden-pallets-with-computer-vision/)

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