Print Error Detection using Convolutional Neural Networks
This addresses the problem of automating print error detection for quality control, but it is incremental as it applies an existing method to a new domain.
The paper tackles automated print error detection by generating an artificial dataset and using a Convolutional Neural Network, achieving a testing accuracy of 99.83%.
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.