Printed Material Quality Evaluation with Scanned-Image Defect Alerts
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Solution Overview
Problem
Existing systems fail to accurately evaluate the quality of printed materials due to defects in scanned images, such as colored dots or lines, which can affect the evaluation results.
Innovation Solution
An information processing system that compares an original print image with a scanned image to evaluate quality, and notifies users of defects that may impact the evaluation, distinguishing between one-off and recurring defects, and providing a notification of their influence on the score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a scanned image is used to evaluate printed material quality, then the evaluation process becomes automated and efficient, but defects in the scanned image (colored dots, white dots, colored lines, white lines) can cause inaccurate evaluation results
Solution Approach 1:
The system performs preliminary defect detection and classification before the quality evaluation is completed. By identifying and flagging defects such as colored dots, white dots, colored lines, and white lines in the scanned image before final quality assessment, the system prevents these defects from causing inaccurate evaluation results while maintaining automated processing efficiency
Solution Approach 2:
The system introduces an intermediary defect detection and notification mechanism between the scanned image and the quality evaluation process. This intermediary layer identifies potential defects, classifies them, and notifies users before the defects can adversely affect the evaluation outcome, thus protecting the measurement precision without sacrificing productivity
2Measurement precision
If defects in scanned images are detected and notified to users, then the accuracy of quality evaluation is improved, but the complexity of the evaluation system increases
Solution Approach 1:
The system segments the defect detection process into distinct functional modules: defect detection, defect classification, and user notification. By dividing the complex task into smaller, manageable segments, the system achieves accurate defect identification while maintaining manageable system complexity through modular architecture
Solution Approach 2:
The system performs self-diagnosis by automatically detecting and classifying defects in the scanned image without requiring external intervention. The defect notification system automatically informs users of detected issues, enabling the system to maintain high evaluation accuracy through autonomous defect management rather than requiring complex manual inspection processes
Data Source
AI summary
An information processing system includes a processor configured to evaluate a quality of a printed material printed by a printing device by comparing an original print image with a scanned image acquired by scanning the printed material, and in a case where a defect that is estimated to affect an evaluation result occurs in the scanned image, notify a user that the defect occurs.