  This paper discusses the need of an automated system for detecting print
errors and the efficacy of Convolutional Neural Networks in such an
application. We recognise the need of a dataset containing print error samples
and propose a way to generate one artificially. We discuss the algorithms to
generate such data along with the limitaions and advantages of such an
apporach. Our final trained network gives a remarkable accuracy of 99.83\% in
testing. We further evaluate how such efficiency was achieved and what
modifications can be tested to further the results.
