Title: SAML-QC: a Stochastic Assessment and Machine Learning based QC technique for Industrial Printing,
Abstract: Recently, the advancement in industrial automation and high-speed printing
has raised numerous challenges related to the printing quality inspection of
final products. This paper proposes a machine vision based technique to assess
the printing quality of text on industrial objects. The assessment is based on
three quality defects such as text misalignment, varying printing shades, and
misprinted text. The proposed scheme performs the quality inspection through
stochastic assessment technique based on the second-order statistics of
printing. First: the text-containing area on printed product is identified
through image processing techniques. Second: the alignment testing of the
identified text-containing area is performed. Third: optical character
recognition is performed to divide the text into different small boxes and only
the intensity value of each text-containing box is taken as a random variable
and second-order statistics are estimated to determine the varying printing
defects in the text under one, two and three sigma thresholds. Fourth: the
K-Nearest Neighbors based supervised machine learning is performed to provide
the stochastic process for misprinted text detection. Finally, the technique is
deployed on an industrial image for the printing quality assessment with
varying values of n and m. The results have shown that the proposed SAML-QC
technique can perform real-time automated inspection for industrial printing.