Abstract: Methods, systems, and non-transitory computer readable media are disclosed for intelligently generating partially textured accessible images. In one or more embodiments, the disclosed systems generate or access a color texture map specific to a given color vision deficiency. For example, the disclosed systems generate a color texture map that divides a color space into one or more textured segment of colors and one or more complementary untextured segments of colors. The disclosed systems can utilize the color texture map to intelligently apply different textures to subsets of pixels that contribute to color vision deficiency confusion. For example, the disclosed system maps pixels from a color image to the color texture map to identify a first subset of pixels corresponding to the textured segment of colors. The disclosed system generates a partially textured accessible image by applying a texture to the first subset of pixels.

Abstract: Methods, systems, and non-transitory computer readable media are disclosed for intelligently generating partially textured accessible images. In one or more embodiments, the disclosed systems generate or access a color texture map specific to a given color vision deficiency. For example, the disclosed systems generate a color texture map that divides a color space into one or more textured segment of colors and one or more complementary untextured segments of colors. The disclosed systems can utilize the color texture map to intelligently apply different textures to subsets of pixels that contribute to color vision deficiency confusion. For example, the disclosed system maps pixels from a color image to the color texture map to identify a first subset of pixels corresponding to the textured segment of colors. The disclosed system generates a partially textured accessible image by applying a texture to the first subset of pixels.

Abstract: Utilizing a visual-feature-classification model to generate font maps that efficiently and accurately organize fonts based on visual similarities. For example, extracting features from fonts of varying styles and utilize a self-organizing map (or other visual-feature-classification model) to map extracted font features to positions within font maps. Further, magnifying areas of font maps by mapping some fonts within a bounded area to positions within a higher-resolution font map. Additionally, navigating the font map to identify visually similar fonts (e.g., fonts within a threshold similarity).

Abstract: Embodiments are disclosed for recommending fonts based on text inputs are described. In some embodiments, a method of recommending fonts includes receiving a selection of text, providing a representation of the selection of text to a font recommendation model, generating, by the font recommendation model, a prediction score for each of a plurality of fonts based on the representation of the selection of text, and returning at least one recommended font based on the prediction score for each of the plurality of fonts.

Abstract: Disclosed systems and methods categorize text regions of an electronic document into document object types based on a combination of semantic information and appearance information from the electronic document. A page segmentation application executing on a computing device provides a textual feature representation and a visual feature representation to a neural network. The application identifies a correspondence between a location of the set of pixels in the electronic document and a location of a particular document object type in an output page segmentation. The application further outputs a classification of the set of pixels as being the particular document object type based on the identified correspondence.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for utilizing a visual-feature-classification model to generate font maps that efficiently and accurately organize fonts based on visual similarities. For example, the disclosed systems can extract features from fonts of varying styles and utilize a self-organizing map (or other visual-feature-classification model) to map extracted font features to positions within font maps. Further, the disclosed systems can also magnify areas of font maps by mapping some fonts within a bounded area to positions within a higher-resolution font map. Additionally, the disclosed systems can navigate the font map to identify visually similar fonts (e.g., fonts within a threshold similarity).

Abstract: Methods and systems disclosed herein relate generally to systems and methods for transforming document elements in response to modifications to a layout of a document. A layout-modification application identifies, from a first document having a first document layout, a first set of measurements of a document element and a first location of the document element within the first document. Based on an aspect-ratio difference between the first document layout and a second document layout, the layout-modification application selects a set of transformation rules that specify, for the document element, a second set of measurements and a second location within a second document. To select the particular set of transformation rules, the layout-modification application uses the determined aspect-ratio difference. The layout-modification application applies the selected set of transformation rules to the document element.

Abstract: Methods and systems for aiding users in generating object pattern designs with increased speed. In particular, one or more embodiments train a sequence-based machine-learning model using training objects, each training object including a plurality of regions with a plurality of design elements. One or more embodiments identify a plurality regions of an object with a first region adjacent a second region. One or more embodiments receive a user selection of a design element for populating the first region with a first design element from a plurality of design elements. One or more embodiments identify a second design element from the plurality of design elements based on the first design element using the trained sequence-based machine-learning model. One or more embodiments also populate the second region with one or more instances of the second design element.

Abstract: The present disclosure relates to utilizing a neural network to flexibly generate label distributions for modifying a segment of text to emphasize one or more words that accurately communicate the meaning of the segment of text. For example, the disclosed systems can utilize a neural network having a long short-term memory neural network architecture to analyze a segment of text and generate a plurality of label distributions corresponding to the words included therein. The label distribution for a given word can include probabilities across a plurality of labels from a text emphasis labeling scheme where a given probability represents the degree to which the corresponding label describes the word. The disclosed systems can modify the segment of text to emphasize one or more of the included words based on the generated label distributions.

Abstract: Certain embodiments involve generating one or more of appearance guide and a positional guide and using one or more of the guides to synthesize a stylized image or animation. For example, a system obtains data indicating a target image and a style exemplar image. The system generates an appearance guide, a positional guide, or both from the target image and the style exemplar image. The system uses one or more of the guides to transfer a texture or style from the style exemplar image to the target image.

Abstract: Disclosed systems and methods generate page segmented documents from unstructured vector graphics documents. The page segmentation application executing on a computing device receives as input an unstructured vector graphics document. The application generates an element proposal for each of many areas on a page of the input document tentatively identified as being page elements. The page segmentation application classifies each of the element proposals into one of a plurality of defined type of categories of page elements. The page segmentation application may further refine at least one of the element proposals and select a final element proposal for each element within the unstructured vector document. One or more of the page segmentation steps may be performed using a neural network.

Abstract: Disclosed systems and methods categorize text regions of an electronic document into document object types based on a combination of semantic information and appearance information from the electronic document. A page segmentation application executing on a computing device provides a textual feature representation and a visual feature representation to a neural network. The application identifies a correspondence between a location of the set of pixels in the electronic document and a location of a particular document object type in an output page segmentation. The application further outputs a classification of the set of pixels as being the particular document object type based on the identified correspondence.

Abstract: Disclosed systems and methods categorize text regions of an electronic document into document object types based on a combination of semantic information and appearance information from the electronic document. A page segmentation application executing on a computing device accesses textual feature representations that represent text portions in a vector space, where a set of pixels from the page is mapped to a textual feature representation. The page segmentation application generates a visual feature representation, which corresponds to an appearance of a document portion including the set of pixels, by applying a neural network to the page of the electronic document. The page segmentation application generates an output page segmentation of the electronic document by applying the neural network to the textual feature representation and the visual feature representation.

Abstract: Certain embodiments involve generating one or more of appearance guide and a positional guide and using one or more of the guides to synthesize a stylized image or animation. For example, a system obtains data indicating a target image and a style exemplar image. The system generates an appearance guide, a positional guide, or both from the target image and the style exemplar image. The system uses one or more of the guides to transfer a texture or style from the style exemplar image to the target image.

Abstract: Systems and methods for computerized drawing of ornamental designs consisting of placed instances of simple shapes. The shapes, called elements, are selected from a small library of templates. The elements are deformed to flow along a direction field interpolated from user-supplied strokes, giving a sense of visual flow to the final composition, and constrained to lie within a container region. In an implementation, a vector field is computed based on user strokes. Streamlines that conform to the vector field are constructed, and an element is placed over each streamline. The shape of the elements may be modified such as by bending, stretching or enlarging to reduce spacing between elements and to minimize variations in spacing to improve aesthetic appearance.

Abstract: Certain embodiments involve generating an appearance guide, a segmentation guide, and a positional guide and using one or more of the guides to synthesize a stylized image or animation. For example, a system obtains data indicating a target and a style exemplar image and generates a segmentation guide for segmenting the target image and the style exemplar image and identifying a feature of the target image and a corresponding feature of the style exemplar image. The system generates a positional guide for determining positions of the target feature and style feature relative to a common grid system. The system generates an appearance guide for modifying intensity levels and contrast values in the target image based on the style exemplar image. The system uses one or more of the guides to transfer a texture of the style feature to the corresponding target feature.

Abstract: Disclosed systems and methods generate page segmented documents from unstructured vector graphics documents. The page segmentation application executing on a computing device receives as input an unstructured vector graphics document. The application generates an element proposal for each of many areas on a page of the input document tentatively identified as being page elements. The page segmentation application classifies each of the element proposals into one of a plurality of defined type of categories of page elements. The page segmentation application may further refine at least one of the element proposals and select a final element proposal for each element within the unstructured vector document. One or more of the page segmentation steps may be performed using a neural network.

Abstract: Disclosed systems and methods generate page segmented documents from unstructured vector graphics documents. The page segmentation application executing on a computing device receives as input an unstructured vector graphics document comprising drawing commands. The application generates an element proposal for each of many areas on a page of the input document tentatively identified as being page elements. Each of the element proposals may be generated at least in part based on the drawing commands. The page segmentation application classifies each of the element proposals into one of a plurality of defined type of categories of page elements at least in part based on the drawing commands. The page segmentation application may further refine at least one of the element proposals and select a final element proposal for each element within the unstructured vector document. One or more of the page segmentation steps may be performed using a neural network.

Abstract: Disclosed systems and methods categorize text regions of an electronic document into document object types based on a combination of semantic information and appearance information from the electronic document. A page segmentation application executing on a computing device accesses textual feature representations that represent text portions in a vector space, where a set of pixels from the page is mapped to a textual feature representation. The page segmentation application generates a visual feature representation, which corresponds to an appearance of a document portion including the set of pixels, by applying a neural network to the page of the electronic document. The page segmentation application generates an output page segmentation of the electronic document by applying the neural network to the textual feature representation and the visual feature representation.

Abstract: Certain embodiments involve generating an appearance guide, a segmentation guide, and a positional guide and using one or more of the guides to synthesize a stylized image or animation. For example, a system obtains data indicating a target and a style exemplar image and generates a segmentation guide for segmenting the target image and the style exemplar image and identifying a feature of the target image and a corresponding feature of the style exemplar image. The system generates a positional guide for determining positions of the target feature and style feature relative to a common grid system. The system generates an appearance guide for modifying intensity levels and contrast values in the target image based on the style exemplar image. The system uses one or more of the guides to transfer a texture of the style feature to the corresponding target feature.