Assessing print quality by machine in offset colour printing

Authors:
J. LundströM;A. Verikas
Affiliations:
Intelligent Systems Laboratory, Halmstad University, Box 823, S 301 18 Halmstad, Sweden;Intelligent Systems Laboratory, Halmstad University, Box 823, S 301 18 Halmstad, Sweden and Department of Electrical & Control Equipment, Kaunas University of Technology, Studentu 50, LT-51368 Kau ...
Venue:
Knowledge-Based Systems
Year:
2013

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Abstract

Information processing steps in printing industry are highly automated, except the last one-print quality assessment, which usually is a manual, tedious, and subjective procedure. This article presents a random forests-based technique for automatic print quality assessment based on objective values of several print quality attributes. Values of the attributes are obtained from soft sensors through data mining and colour image analysis. Experimental investigations have shown good correspondence between print quality evaluations obtained by the technique proposed and the average observer.