Abstract: An example method includes capturing a target image of a print product printed by a printer. The method also includes aligning the target image with a reference image corresponding to the target image. The method further includes analyzing the reference image and the target image using a machine learning model. The method includes labeling each of a plurality of pixels as having a defect based on the analysis. The label is applied to each individual pixel of the plurality of pixels.

Abstract: An example of an apparatus is provided. The apparatus includes an extraction engine to extract a plurality of patches from an image of a printed document. The apparatus further includes a classification engine to analyze each patch of the plurality of patches and to assign a defect probability to each patch of the plurality of patches. The apparatus also includes a rendering engine to generate a map based on the defect probability of each patch of the plurality of patches. The map is to identify defects in the printed document.

Abstract: An example of an apparatus is provided. The apparatus includes a preprocessing engine to preprocess an image of a print into a reduced format wherein the reduced format includes a plurality of pixels. The apparatus further includes a segmentation analysis engine to generate a plurality of labels. Each label of the plurality of labels is associated with a pixel of the plurality of pixels. The plurality of labels identifies each pixel of the plurality of pixels as a defective pixel or a non-defective pixel. The apparatus also includes a rendering engine to display defects in the reduced format based on the plurality of labels.