Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating and providing matching fonts by utilizing a glyph-based machine learning model. For example, the disclosed systems can generate a glyph image by arranging glyphs from a digital document according to an ordering rule. The disclosed systems can further identify target fonts as fonts that include the glyphs within the glyph image. The disclosed systems can further generate target glyph images by arranging glyphs of the target fonts according to the ordering rule. Based on the glyph image and the target glyph images, the disclosed systems can utilize a glyph-based machine learning model to generate and compare glyph image feature vectors. By comparing a glyph image feature vector with a target glyph image feature vector, the font matching system can identify one or more matching glyphs.

Abstract: Disclosed herein are various techniques for more precisely and reliably (a) positioning top and bottom border edges relative to textual content, (b) positioning left and right border edges relative to textual content, (c) positioning mixed edge borders relative to textual content, (d) positioning boundaries of a region of background shading that fall within borders of textual content, (e) positioning borders relative to textual content that spans columns, (f) positioning respective borders relative to discrete portions of textual content, (g) positioning collective borders relative to discrete, abutting portions of textual content, (h) applying stylized corner boundaries to a region of background shading, and (i) applying stylized corners to borders.

Abstract: A method, system, and computer program product for correlating dynamic device configurations from multiple sources. The method may include identifying device management settings from a device management system. The method may also include receiving source settings from a second source. The method may also include analyzing individual words from the device management settings and the source settings. The method may also include analyzing strings, integers, and Booleans from the device management settings and the source settings. The method may also include identifying, based on the analyzing individual words and the analyzing strings, integers, and Booleans, corresponding settings from the device management settings and the source settings. The method may also include determining that the corresponding settings are conflicting settings. The method may also include flagging, based on the determining, conflicts of the corresponding settings.

Abstract: Disclosed herein are various techniques for more precisely and reliably (a) positioning top and bottom border edges relative to textual content, (b) positioning left and right border edges relative to textual content, (c) positioning mixed edge borders relative to textual content, (d) positioning boundaries of a region of background shading that fall within borders of textual content, (e) positioning borders relative to textual content that spans columns, (f) positioning respective borders relative to discrete portions of textual content, (g) positioning collective borders relative to discrete, abutting portions of textual content, (h) applying stylized corner boundaries to a region of background shading, and (i) applying stylized corners to borders.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating and providing matching fonts by utilizing a glyph-based machine learning model. For example, the disclosed systems can generate a glyph image by arranging glyphs from a digital document according to an ordering rule. The disclosed systems can further identify target fonts as fonts that include the glyphs within the glyph image. The disclosed systems can further generate target glyph images by arranging glyphs of the target fonts according to the ordering rule. Based on the glyph image and the target glyph images, the disclosed systems can utilize a glyph-based machine learning model to generate and compare glyph image feature vectors. By comparing a glyph image feature vector with a target glyph image feature vector, the font matching system can identify one or more matching glyphs.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating and providing matching fonts by utilizing a glyph-based machine learning model. For example, the disclosed systems can generate a glyph image by arranging glyphs from a digital document according to an ordering rule. The disclosed systems can further identify target fonts as fonts that include the glyphs within the glyph image. The disclosed systems can further generate target glyph images by arranging glyphs of the target fonts according to the ordering rule. Based on the glyph image and the target glyph images, the disclosed systems can utilize a glyph-based machine learning model to generate and compare glyph image feature vectors. By comparing a glyph image feature vector with a target glyph image feature vector, the font matching system can identify one or more matching glyphs.

Abstract: In implementations of text wrap detection, one or more computing devices of a system implement a text wrap module for detecting text wrap around a component of digital content of a document. The document is preprocessed to segregate the digital content into a text group and a non-text group. Members of the text group are overlaid with a graphical element colored to provide a contrast between the graphical element and the component of the digital content. The document is converted to a digital image and a feature map of the digital image is generated. The feature map is further processed using machine learning and a detection indication is output. The detection indication may indicate that text wrap is detected around a member of the text group, a member of the non-text group, or that no text wrap is detected.

Abstract: In implementations of text wrap detection, one or more computing devices of a system implement a text wrap module for detecting text wrap around a component of digital content of a document. The document is preprocessed to segregate the digital content into a text group and a non-text group. Members of the text group are overlaid with a graphical element colored to provide a contrast between the graphical element and the component of the digital content. The document is converted to a digital image and a feature map of the digital image is generated. The feature map is further processed using machine learning and a detection indication is output. The detection indication may indicate that text wrap is detected around a member of the text group, a member of the non-text group, or that no text wrap is detected.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating and providing matching fonts by utilizing a glyph-based machine learning model. For example, the disclosed systems can generate a glyph image by arranging glyphs from a digital document according to an ordering rule. The disclosed systems can further identify target fonts as fonts that include the glyphs within the glyph image. The disclosed systems can further generate target glyph images by arranging glyphs of the target fonts according to the ordering rule. Based on the glyph image and the target glyph images, the disclosed systems can utilize a glyph-based machine learning model to generate and compare glyph image feature vectors. By comparing a glyph image feature vector with a target glyph image feature vector, the font matching system can identify one or more matching glyphs.

Abstract: Object interaction preservation from design to digital publication is described. In example embodiments, a design includes overlapping objects with object interaction effects. The design has a document object model (DOM). A digital publishing module constructs a DOM tree with each node having an object of the design. The DOM tree is analyzed to identify objects that interact with an overlapped object. Objects with dynamic content and those with no overlapped object can be excluded. A bounding region of each identified object is investigated with regard to object layering data and object interaction attributes. Based on these design aspects, the digital publishing module prepares an object snapshot of the bounding region to reflect an appearance resulting from the interactive objects within the bounding region. The object snapshot is incorporated into a digital publication DOM tree while maintaining the object layering data. Using this DOM tree, the module produces a digital publication.

Abstract: Disclosed herein are various techniques for more precisely and reliably (a) positioning top and bottom border edges relative to textual content, (b) positioning left and right border edges relative to textual content, (c) positioning mixed edge borders relative to textual content, (d) positioning boundaries of a region of background shading that fall within borders of textual content, (e) positioning borders relative to textual content that spans columns, (f) positioning respective borders relative to discrete portions of textual content, (g) positioning collective borders relative to discrete, abutting portions of textual content, (h) applying stylized corner boundaries to a region of background shading, and (i) applying stylized corners to borders.

Abstract: Object interaction preservation from design to digital publication is described. In example embodiments, a design includes overlapping objects with object interaction effects. The design has a document object model (DOM). A digital publishing module constructs a DOM tree with each node having an object of the design. The DOM tree is analyzed to identify objects that interact with an overlapped object. Objects with dynamic content and those with no overlapped object can be excluded. A bounding region of each identified object is investigated with regard to object layering data and object interaction attributes. Based on these design aspects, the digital publishing module prepares an object snapshot of the bounding region to reflect an appearance resulting from the interactive objects within the bounding region. The object snapshot is incorporated into a digital publication DOM tree while maintaining the object layering data. Using this DOM tree, the module produces a digital publication.