Print Error Detection using Convolutional Neural Networks

TL;DR

This paper explores using convolutional neural networks for automated print error detection, proposing a method to generate training data and achieving high accuracy of 99.83% in testing.

Contribution

It introduces a novel approach to generate print error datasets artificially and demonstrates the effectiveness of CNNs in detecting print errors.

Findings

Achieved 99.83% accuracy in print error detection

Developed a method to generate print error datasets artificially

Analyzed modifications to improve CNN performance

Abstract

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.