A display processing method is provided, comprising: displaying, by an electronic device, a first display object and a second display object on a first screen based on a first positional relationship; in response to determining that a length of a to-be-displayed character string corresponding to the first display object is greater than a first width of the first display object on the first screen, stopping displaying the first screen; and displaying the first display object and the second display object on a second screen, where the first display object and the second display object on the second screen have a second positional relationship, the second positional relationship is different from the first positional relationship, and a second width of the first display object corresponding to the second positional relationship is greater than the first width.

The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately and flexibly generating scalable and semantically editable font representations utilizing a machine learning approach. For example, the disclosed systems generate a font representation code from a glyph utilizing a particular neural network architecture. For example, the disclosed systems utilize a glyph appearance propagation model and perform an iterative process to generate a font representation code from an initial glyph. Additionally, using a glyph appearance propagation model, the disclosed systems automatically propagate the appearance of the initial glyph from the font representation code to generate additional glyphs corresponding to respective glyph labels. In some embodiments, the disclosed systems propagate edits or other changes in appearance of a glyph to other glyphs within a glyph set (e.g., to match the appearance of the edited glyph).

The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately and flexibly generating scalable and semantically editable font representations utilizing a machine learning approach. For example, the disclosed systems generate a font representation code from a glyph utilizing a particular neural network architecture. For example, the disclosed systems utilize a glyph appearance propagation model and perform an iterative process to generate a font representation code from an initial glyph. Additionally, using a glyph appearance propagation model, the disclosed systems automatically propagate the appearance of the initial glyph from the font representation code to generate additional glyphs corresponding to respective glyph labels. In some embodiments, the disclosed systems propagate edits or other changes in appearance of a glyph to other glyphs within a glyph set (e.g., to match the appearance of the edited glyph).

The present invention relates to a system and method for synchronizing and highlighting a target text and audio associated with a reference document. The system and method may comprise one or more of an input unit, an extracting unit, a mapping unit, a processing unit, and an image resizing unit. The system and method may synchronize the target text and audio in order to provide a user with a Read Along. The invention further synchronizes and highlights closed captions and audio that helps people with hearing impairment to comprehend better while watching a movie or listening to songs.

The present invention relates to a system and method for synchronizing and highlighting a target text and audio associated with a reference document. The system and method may comprise one or more of an input unit, an extracting unit, a mapping unit, a processing unit, and an image resizing unit. The system and method may synchronize the target text and audio in order to provide a user with a Read Along. The invention further synchronizes and highlights closed captions and audio that helps people with hearing impairment to comprehend better while watching a movie or listening to songs.

A system includes a computing device that includes a memory configured to store instructions. The computing device also includes a processor to execute the instructions to perform operations that include receiving data representing one or more user-selected item attributes. The data includes one of at least four selectable interest levels for each of the one or more user-selected item attributes. Operations also include identifying one or more items representative of the selected interest level for each of the one or more user-selected item attributes, and initiating delivery of data representing the identified one or more items for user selection.

A system includes a computing device that includes a memory configured to store instructions. The computing device also includes a processor to execute the instructions to perform operations that include receiving data representing one or more user-selected item attributes. The data includes one of at least four selectable interest levels for each of the one or more user-selected item attributes. Operations also include identifying one or more items representative of the selected interest level for each of the one or more user-selected item attributes, and initiating delivery of data representing the identified one or more items for user selection.

A method for tailoring a document for viewing on a mobile device includes obtaining residence information for an individual who is required to sign the document. The document is tailored to the residence of the individual. This includes hiding or condensing one or more portions of the document that pertain to laws in other jurisdictions. The document is permitted to be accessible by the mobile device.

A method for tailoring a document for viewing on a mobile device includes obtaining residence information for an individual who is required to sign the document. The document is tailored to the residence of the individual. This includes hiding or condensing one or more portions of the document that pertain to laws in other jurisdictions. The document is permitted to be accessible by the mobile device.

In aspects of text importance spatial layout, a computing device implements a processing device to receive a text importance vector that includes designations of visual properties for constituent words of a text phrase. Spatial layouts of the text phrase are determined, with each spatial layout being a different displayable representation of the constituent words arranged based on the designations of the visual properties in the text importance vector for each of the constituent words. Feature vectors are generated, each feature vector represents a spatial layout of the text phrase and includes measurement properties of each of the constituent words in the respective spatial layout. The spatial layouts are ranked based on a metric that indicates a degree of similarity of the measurement properties of each of the constituent words in a respective spatial layout matching to the visual properties for the constituent words as designated in the text importance vector.