Adobe, Inc.

Amish Kumar Bedi

Sanyam Jain

Monica Singh

Prateek Gaurav

Jianming Zhang

Gaurav Bhargava

Zoya Bylinskii

Damanpreet Kaur

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.

Identifying matching fonts utilizing deep learning

This disclosure includes technologies for image processing, specifically for generating layout variations that are different but visually consistent with the input layout. The disclosed system determines a visual flow of the design blocks in the input layout, and then generates layout variations based on the visual flow. Advantageously, the disclosed technologies enable both novices and seasoned designers to efficiently create alternative layout variations, even in real-time with intricate designs.

Automatic generation of layout variations based on visual flow

Utilizing glyph-based machine learning models to generate matching fonts

Disclosed systems and methods for the automatic creation of multiple layouts that maintain a design aesthetic of an input design document. In an example, a document processing application determines a set of document layout parameters such as balance or equilibrium from an input document. The application calculates, for each document layout parameter of the input document, a weight representing a prominence of the respective layout parameter. The application selects templates having an output size and a number of object containers equal to the number of objects of the document. The application further calculates a score for each template by applying the weights of the document layout parameters to the template layout parameters. The application further selects a template with a highest score and places the object on the template, thereby creating the new design document that maintains the design aesthetic.

Automatic generation of document layouts

Systems and methods are described for dynamically fitting a digital image based on the saliency of the image and the aspect ratio of a frame are described. The systems and methods may provide for identifying an aspect ratio of the frame, selecting a salient region of the digital image based on the aspect ratio using a saliency prediction model, and fitting the digital image into the frame so that a boundary of the frame is aligned with a boundary of the salient region.

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Naya Nangal, India

Amish Kumar Bedi