Print ROI Defect Detection for Color Text Fading
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Solution Overview
Problem
Existing print defect detection techniques often require dedicated test images, leading to resource wastage and inefficiency, and struggle to detect color text fading effectively.
Innovation Solution
The use of a scanned image to identify defects through a processor that applies the SLIC Super-pixels algorithm and clustering classification for color fading, and aligns text characters to detect text fading, predicting depleted cartridges and other printer issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dedicated test images are used for defect detection, then measurement precision is improved, but loss of substance increases due to resource wastage
Solution Approach 1:
The printer's imaging system is made multi-functional by enabling it to perform both normal printing operations and defect detection using the same scanned images. The processor analyzes scanned images for defects while the printer continues its primary printing function, eliminating the need for separate dedicated test images and reducing resource consumption.
Solution Approach 2:
The system uses its own operational output (scanned images of printed pages) to perform self-diagnosis and defect detection. By analyzing images that are already being captured for other purposes, the printer monitors its own print quality without requiring additional test prints or external resources.
2Productivity
If traditional defect detection methods are used, then device complexity is reduced, but productivity decreases due to inefficiency
Solution Approach 1:
The system performs preliminary defect detection during the normal printing workflow by analyzing scanned images as they are captured. Rather than requiring a separate detection step, the defect analysis is integrated into the existing printing and scanning process, improving efficiency without adding significant complexity.
Solution Approach 2:
The patent replaces physical test image printing and manual inspection with automated digital image processing. The processor uses algorithms to automatically detect defects in scanned images, substituting mechanical printing and human analysis with computational methods that improve productivity.
3Measurement precision
If simple detection methods are used, then device complexity is reduced, but measurement precision worsens for color text fading
Solution Approach 1:
The system applies specialized processing tailored to specific defect types. For color text fading detection, the processor uses clustering algorithms and color space analysis specifically optimized for text regions, while other areas of the image may use different processing approaches. This localized optimization improves precision for color text fading without unnecessarily complicating the entire system.
Solution Approach 2:
The patent transforms the detection problem by changing color parameters and using clustering in color space. Instead of simple intensity comparison, the system converts images to different color spaces and applies clustering algorithms to detect subtle color variations in text, achieving high precision for color text fading detection through parameter transformation.
Data Source
AI summary
According to examples, an apparatus may include a processor and a memory on which are stored computer-readable instructions that, when executed by the processor, may cause the processor to access a master image to be printed and receive a scanned image corresponding to the master image. The processor may identify a defect in a scanned image region of interest (ROI) based on the master image. A type of the scanned image ROI may include a raster ROI, a symbol ROI, a background ROI, and/or a color vector ROI, and the identified defect may be associated with the type of the scanned image ROI. The processor may determine a severity of the identified defect of the scanned image and may output the determined severity.