An automatic method for detecting missing dots in rotogravure printing

Abstract

We introduce a new approach to detecting missing dots in rotogravure printing, based on binary morphology and convolution filtering, that addresses the drawbacks of automated techniques using a fast Fourier transform algorithm. Our method was developed for the Heliotest, which is a widely used halftone print quality test for rotogravure printing. A strip of decreasing halftone intensity is printed and the missing dots are counted visually. The distance to the 20th missing dot provides the Heliotest print quality number. To automate the determination of the Heliotest number, the strip is scanned with a standard flat-bed scanner. Using image analysis, an image binarization separates the printed dots from the unprinted area. Then, a black and white convolution is performed to locate the specific shape corresponding to the missing dots. Each possible missing dot is analyzed by the algorithm to determine whether it is a missing dot or a dot that is misshapen, weak, or incomplete. We found that this automatic procedure provides an excellent match to visual assessment. On 90 out of 100 test samples used for validation, our method gives the same result as the visual assessment. The other samples have one or two false positives in the missing dots. This was significantly better than the techniques that were in use before. After validation, the technique was deployed at NewPage; it has been regularly used as a product quality test at the mill and at the NewPage Research Center since 2011.