5. Conclusions

This chapter proposes a stochastic assessment and 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. It is concluded from the results that second-order statistics related to the intensity values of pixels of text located under the threshold region of the original image can provide enough information to perform quality inspection. On the other hand, the hamming distance acquired by the k-NN supervised machine learning can also be taken as a random variable, and again the secondorder statistics are helpful in order to detect the misprinted letter. In the future SAML-QC technique needs to be evaluated on various other images such as electronics parts and medical parts.
