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Recently, the advancement in industrial automation and high-speed printinghas raised numerous challenges related to the printing quality inspection offinal products. This paper proposes a machine vision based technique to assessthe printing quality of text on industrial objects. The assessment is based onthree quality defects such as text misalignment, varying printing shades, andmisprinted text. The proposed scheme performs the quality inspection throughstochastic assessment technique based on the second-order statistics ofprinting. First: the text-containing area on printed product is identifiedthrough image processing techniques. Second: the alignment testing of theidentified text-containing area is performed. Third: optical characterrecognition is performed to divide the text into different small boxes and onlythe intensity value of each text-containing box is taken as a random variableand second-order statistics are estimated to determine the varying printingdefects in the text under one, two and three sigma thresholds. Fourth: theK-Nearest Neighbors based supervised machine learning is performed to providethe stochastic process for misprinted text detection. Finally, the technique isdeployed on an industrial image for the printing quality assessment withvarying values of n and m. The results have shown that the proposed SAML-QCtechnique can perform real-time automated inspection for industrial printing.

SAML-QC: a Stochastic Assessment and Machine Learning based QC technique for Industrial Printing