Adobe, Inc.

Shikhar Garg

Saikat Chakrabarty

Nirmal Kumawat

Neel Kadia

Sourabh Gupta

Jose Ignacio Echevarria Vallespi

Mrinal Kumar Sharma

Gourav Singhal

Techniques for content-aware font recommendations include obtaining an electronic document comprising an image and text. The image is processed using one or more convolutional neural networks to determine one or more image tags. The image tags are mapped to one or more font tags using a user map, a designer map, or one or more contextual synonyms of the image tags. A font to recommend for the electronic document is then determined using the one or more font tags.

Content aware font recommendation

In implementations of systems for searching for images using generated images, a computing device implements a search system to receive a natural language search query for digital images included in a digital image repository. The search system generates a set of digital images using a machine learning model based on the natural language search query. The machine learning model is trained on training data to generate digital images based on natural language inputs. The search system performs an image-based search for digital images included in the digital image repository using the set of digital images. An indication of the search result is generated for display in a user interface based on performing the image-based search.

Searching for images using generated images

A curve sampling technique for generating transformed digital visual content is leveraged in a digital medium environment. Initially, a curve sampling system obtains digital visual content, e.g., images and videos. The curve sampling system generates transformed digital visual content by transforming one or more pixels of the digital visual content using a lookup table that is derived from samples of a curve taken at evenly spaced intervals along a y-axis of a graph of the curve. Broadly speaking, the curve defines how to transform a visual characteristic of the pixels in order to achieve a desired digital visual content transformation. Additionally, the curve sampling may correspond to one step in a series of steps for transforming colors of digital visual content. Indeed, such transformations may involve multiple curve sampling steps.

Evenly spaced curve sampling technique for digital visual content transformation

Font recommendation systems and techniques are provided for determining one or more font recommendations. Content aware font recommendations can be determined for an electronic document based on information from the design context of the electronic document. For example, to generate recommended fonts for an electronic document, the font recommendation systems and techniques can obtain one or images of the electronic document and/or text from the electronic document as input, and can process the one or more images and/or the text to determine recommended font tags. In some cases, recommended fonts can be determined based on user search queries, such as when a user searches for a particular font.

Embodiments are disclosed for determining a context-aware text color recommendation for text at a text location on an image. In particular, in one or more embodiments, the disclosed systems and methods comprise obtaining an image and a text location on the image, identifying at least one color theme based on a color harmonic template associated with the image, modifying the at least one color theme based on characteristics of the image, determining accessibility for at least one color in the at least one color theme based on the text location on the image, and determining a color palette recommendation for text at the text location on the image based on the determined accessibility for the at least one color in the at least one color theme.

Context-aware text color recommendation system

In implementations of image content snapping guidelines, a guidelines segmentation system includes modules, such as an image pre-processing module to reduce the image size of a digital image if the image size exceeds an image size threshold. An object segmentation module segments objects depicted in the digital image and identifies each object by a bounding border that delineates an object region boundary. An edge detection module receives a segmented object and determines object external edges and object feature edges from the segmented object, and identifies object corners of the object. A snapping guidelines module determines image content snapping guidelines of an object depicted in the digital image, the image content snapping guidelines for an object determined based on the bounding border of the object region boundary, the object external edges, the object feature edges, and projected snapping guidelines that extend from the object corners of the object.

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New Delhi, India

Shikhar Garg