Abstract: Systems and methods for determining the priority of a set of reports available to a user. The method comprising determining, by a reporting application, a set of reports available to a user, each report including one or more metrics. The method further comprising obtaining metric data associated with each of the set of reports available to the user, accessing one or more priority calculation parameters for each report in the set of reports, calculating a priority value of each report of the set of reports based on the metric data and the priority calculation parameters for each report, and determining one or more high priority reports based on the priority value of each report of the set of reports.

Abstract: In implementations of pattern filling in garment pieces, a pattern filling system represents a first garment piece as a first curve and a second garment piece as a second curve. The system determines a relationship between the first garment piece and the second garment piece by comparing the first curve and the second curve. The pattern filling system generates a visually pleasing fill pattern for the first garment piece and the second garment piece based on the determined relationship.

Abstract: Techniques and systems are described for automatic artifact removal in a digital image. A segmentation map is generated that describes a magnitude of difference among pixels in a digital image. Contours may be generated that describe boundaries of objects described in the segmentation map. The contours may be filtered according to two-dimensional and three-dimensional cues to identify contours corresponding to artifacts in the digital image. For each contour corresponding to an artifact, an object mask and a sampling mask may be generated. The object mask and the sampling mask may be utilized as part of a content filling operation upon the digital image to remove the artifact, and a corrected digital image is generated that does not include the artifact.

Abstract: In implementations of systems for font replacement in print workflows, a computing device implements a print system to receive print request data describing a document having a corpus of text rendered using a font that is not available to the print system. The print system extracts the corpus of text from the document and generates an indication of a context category of the corpus of text using a machine learning model training to classify context categories of text inputs using training data describing a different corpus of text. A replacement font is identified based on the indication of the context category from replacement font data describing a plurality of candidate replacement fonts. The printing system generates a raster image depicting the corpus of text rendered using the replacement font.

Abstract: Automatic positioning of textual content within digital images is leveraged in a digital medium environment. Initially, user input is received to add textual content to a digital image. The digital image can then be processed to identify at least one object in the digital image using an image segmentation model. A placement region for the textual content that does not overlap the at least one object can be automatically determined. After the placement region is automatically determined, the digital image can be modified by positioning the textual content within the automatically determined placement region of the digital image. Positioning the textual content may include automatically adjusting the textual content to fit within the placement region, such as by automatically scaling or aligning the textual content.

Abstract: In implementations of systems for font replacement in print workflows, a computing device implements a print system to receive print request data describing a document having a corpus of text rendered using a font that is not available to the print system. The print system extracts the corpus of text from the document and generates an indication of a context category of the corpus of text using a machine learning model training to classify context categories of text inputs using training data describing a different corpus of text. A replacement font is identified based on the indication of the context category from replacement font data describing a plurality of candidate replacement fonts. The printing system generates a raster image depicting the corpus of text rendered using the replacement font.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing intelligent sectioning and selective document reflow for section-based printing. For example, the disclosed systems can intelligently identify document objects (e.g., document structures and sections) within a digital document by utilizing a machine-learning model. In so doing, the disclosed systems can identify document-object types and document-object locations for the document objects in the digital document. In turn, the disclosed systems can provide, for display within a dynamic printing interface, selectable document sections comprising the identified document objects. In response to a user selection of one or more of the selectable document sections, the disclosed system can generate a modified digital document for printing by reflowing the identified document objects in accordance with the user selection.

Abstract: Techniques and systems are described for automatic artifact removal in a digital image. A segmentation map is generated that describes a magnitude of difference among pixels in a digital image. Contours may be generated that describe boundaries of objects described in the segmentation map. The contours may be filtered according to two-dimensional and three-dimensional cues to identify contours corresponding to artifacts in the digital image. For each contour corresponding to an artifact, an object mask and a sampling mask may be generated. The object mask and the sampling mask may be utilized as part of a content filling operation upon the digital image to remove the artifact, and a corrected digital image is generated that does not include the artifact.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing intelligent sectioning and selective document reflow for section-based printing. For example, the disclosed systems can intelligently identify document objects (e.g., document structures and sections) within a digital document by utilizing a machine-learning model. In so doing, the disclosed systems can identify document-object types and document-object locations for the document objects in the digital document. In turn, the disclosed systems can provide, for display within a dynamic printing interface, selectable document sections comprising the identified document objects. In response to a user selection of one or more of the selectable document sections, the disclosed system can generate a modified digital document for printing by reflowing the identified document objects in accordance with the user selection.

Abstract: A method, apparatus, and non-transitory computer readable medium for color reduction based on image segmentation are described. The method, apparatus, and non-transitory computer readable medium may provide for segmenting an input image into a plurality of regions, assigning a weight to each region, identifying one or more colors for each of the regions, selecting a color palette based on the one or more colors for each of the regions and the corresponding weight for each of the regions, and performing a color reduction on the input image using the selected color palette to produce a color reduced image. The weight assigned to each region may depend on factors including relevance, prominence, focus, position, or any combination thereof.

Abstract: Automatic positioning of textual content within digital images is leveraged in a digital medium environment. Initially, user input is received to add textual content to a digital image. The digital image can then be processed to identify at least one object in the digital image using an image segmentation model. A placement region for the textual content that does not overlap the at least one object can be automatically determined. After the placement region is automatically determined, the digital image can be modified by positioning the textual content within the automatically determined placement region of the digital image. Positioning the textual content may include automatically adjusting the textual content to fit within the placement region, such as by automatically scaling or aligning the textual content.

Abstract: In implementations of pattern filling in garment pieces, a pattern filling system represents a first garment piece as a first curve and a second garment piece as a second curve. The system determines a relationship between the first garment piece and the second garment piece by comparing the first curve and the second curve. The pattern filling system generates a visually pleasing fill pattern for the first garment piece and the second garment piece based on the determined relationship.

Abstract: A method, apparatus, and non-transitory computer readable medium for color reduction based on image segmentation are described. The method, apparatus, and non-transitory computer readable medium may provide for segmenting an input image into a plurality of regions, assigning a weight to each region, identifying one or more colors for each of the regions, selecting a color palette based on the one or more colors for each of the regions and the corresponding weight for each of the regions, and performing a color reduction on the input image using the selected color palette to produce a color reduced image. The weight assigned to each region may depend on factors including relevance, prominence, focus, position, or any combination thereof.

Abstract: Techniques are described for obtaining at least one image using at least one processor. The techniques may include selecting a plurality of objects defined by a plurality of pixels within the at least one image. The techniques may include determining perceptual information associated with each of the plurality of objects and generating a context for each of the plurality of pixels that define the plurality of objects. The techniques may also include automatically thresholding the at least one image to generate an output image that represents the at least one image.

Abstract: Techniques are described for obtaining at least one image using at least one processor. The techniques may include selecting a plurality of objects defined by a plurality of pixels within the at least one image. The techniques may include determining perceptual information associated with each of the plurality of objects and generating a context for each of the plurality of pixels that define the plurality of objects. The techniques may also include automatically thresholding the at least one image to generate an output image that represents the at least one image.

Abstract: Techniques for removal of interference patterns from digital images are described, in which a spatially-adaptive filter is applied to a pixel based on a context of the pixel. In an example, an edge of an object in a digital image is located in a digital image creation system. Then, context data is generated for a pixel in the digital image. The context data includes a distance from the edge of the object to the pixel. The digital image creation system can also generate color data and luminance data for the pixel, representing a similarity of color and luminance between the pixel and surrounding pixels within the digital image. Then, the digital image creation system constructs a spatially-adaptive filter for the pixel based on the context data for the pixel. The digital image creation system removes an effect of the interference pattern at the pixel in the digital image by applying the spatially-adaptive filter to the pixel.