  Efficient automated print defect mapping is valuable to the printing industry
since such defects directly influence customer-perceived printer quality and
manually mapping them is cost-ineffective. Conventional methods consist of
complicated and hand-crafted feature engineering techniques, usually targeting
only one type of defect. In this paper, we propose the first end-to-end
framework to map print defects at pixel level, adopting an approach based on
semantic segmentation. Our framework uses Convolutional Neural Networks,
specifically DeepLab-v3+, and achieves promising results in the identification
of defects in printed images. We use synthetic training data by simulating two
types of print defects and a print-scan effect with image processing and
computer graphic techniques. Compared with conventional methods, our framework
is versatile, allowing two inference strategies, one being near real-time and
providing coarser results, and the other focusing on offline processing with
more fine-grained detection. Our model is evaluated on a dataset of real
printed images.
