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Inspection of electrophotographic print cartridge quality and compatibility requires analysis of hundreds of pages on a wide population of printers and copiers. Although print quality inspection is often achieved through the use of anchor prints and densitometry, more comprehensive analysis and quantitative data is desired for performance tracking, benchmarking and failure mode analysis. Image quality measurement systems range in price and performance, image capture paths and levels of automation. In order to address the requirements of a specific application, careful consideration was made to print volume, budgetary limits, and the scope of the desired image quality measurements. A flatbed scanner-based image quality measurement system was selected to support high throughput, maximal automation, and sufficient flexibility for both measurement methods and image sampling rates. Using an automatic document feeder (ADF) for sample management, a half ream of prints can be measured automatically without operator intervention. The system includes optical character recognition (OCR) for automatic determination of target type for measurement suite selection. This capability also enables measurement of mixed stacks of targets since each sample is identified prior to measurement. In addition, OCR is used to read toner ID, machine ID, print count, and other pertinent information regarding the printing conditions and environment. This data is saved to a data file along with the measurement results for complete test documentation. Measurement methods were developed to replace current methods of visual inspection and densitometry. The features that were being analyzed visually could be addressed via standard measurement algorithms. Measurement of density proved to be less simple since the scanner is not a densitometer and anything short of an excellent estimation would be meaningless. In order to address the measurement of density, a transfer curve was built to translate the gray scale values that are the fundamental scanner output into an estimation of density. The correlation coefficient was excellent and was deemed to be adequate for this application.
This paper addresses the automation process, image analysis and data management required by this application.
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