Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed that detect lines in a document. An example apparatus includes at least one memory; machine readable instructions; and processor circuitry to at least one of instantiate or execute the machine readable instructions to: generate feature embeddings for text segments detected in a document image, the segments associated with respective bounding boxes, wherein the segments are to be represented by nodes in a graph; identify candidate edges between ones of the segments; pass the feature embeddings through graph attention layers (GAT) to update the feature embeddings with information from neighbor nodes; generate an adjacency matrix for the document image by determining confidence scores for the edges; and cluster the nodes based on the adjacency matrix to group ones of the nodes that belong to a same line.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to tag segments in a document. An example apparatus includes processor circuitry to execute machine readable instructions to generate node embeddings for nodes of a graph, the node embeddings based on features extracted from text segments detected in a document, the text segments to be represented by the nodes of the graph; sample edges corresponding to the nodes to generate the graph; generate first updated node embeddings by passing the node embeddings and the graph through layers of a graph neural network, the first updated embeddings corresponding to the node embeddings augmented with neighbor information; generate second updated node embeddings by passing the first updated embeddings through layers of a recurrent neural network, the second updated embeddings corresponding to the first updated node embeddings augmented with sequential information; and classify the text segments based on the second updated node embeddings.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to tag segments in a document. An example apparatus includes processor circuitry to execute machine readable instructions to generate node embeddings for nodes of a graph, the node embeddings based on features extracted from text segments detected in a document, the text segments to be represented by the nodes of the graph; sample edges corresponding to the nodes to generate the graph; generate first updated node embeddings by passing the node embeddings and the graph through layers of a graph neural network, the first updated embeddings corresponding to the node embeddings augmented with neighbor information; generate second updated node embeddings by passing the first updated embeddings through layers of a recurrent neural network, the second updated embeddings corresponding to the first updated node embeddings augmented with sequential information; and classify the text segments based on the second updated node embeddings.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed that determine related content.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed that determine related content.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to tag segments in a document. An example apparatus includes processor circuitry to execute machine readable instructions to generate node embeddings for nodes of a graph, the node embeddings based on features extracted from text segments detected in a document, the text segments to be represented by the nodes of the graph; sample edges corresponding to the nodes to generate the graph; generate first updated node embeddings by passing the node embeddings and the graph through layers of a graph neural network, the first updated embeddings corresponding to the node embeddings augmented with neighbor information; generate second updated node embeddings by passing the first updated embeddings through layers of a recurrent neural network, the second updated embeddings corresponding to the first updated node embeddings augmented with sequential information; and classify the text segments based on the second updated node embeddings.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed that determine related content.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed that detect lines in a document. An example apparatus includes at least one memory; machine readable instructions; and processor circuitry to at least one of instantiate or execute the machine readable instructions to: generate feature embeddings for text segments detected in a document image, the segments associated with respective bounding boxes, wherein the segments are to be represented by nodes in a graph; identify candidate edges between ones of the segments; pass the feature embeddings through graph attention layers (GAT) to update the feature embeddings with information from neighbor nodes; generate an adjacency matrix for the document image by determining confidence scores for the edges; and cluster the nodes based on the adjacency matrix to group ones of the nodes that belong to a same line.

Abstract: The present disclosure relates to an AR animation generation system that detects a change in position of a mobile computing system in a real-world environment, determines that a position for a virtual object in an augmented reality (AR) scene is to be changed from a first position in the AR scene to a second position in the AR scene, identifies an animation profile to be used for animating the virtual object, wherein the animation profile is associated with the virtual object, and animates the virtual object in the AR scene using the animation profile. Animating the virtual object in the AR scene includes moving the virtual object in the AR scene from the first position to the second position along a path, wherein the path and a movement of the virtual object along the path are determined based on the animation profile.

Abstract: The present disclosure relates to an AR animation generation system that detects a change in position of a mobile computing system in a real-world environment, determines that a position for a virtual object in an augmented reality (AR) scene is to be changed from a first position in the AR scene to a second position in the AR scene, identifies an animation profile to be used for animating the virtual object, wherein the animation profile is associated with the virtual object, and animates the virtual object in the AR scene using the animation profile. Animating the virtual object in the AR scene includes moving the virtual object in the AR scene from the first position to the second position along a path, wherein the path and a movement of the virtual object along the path are determined based on the animation profile.

Abstract: The present disclosure relates to an AR animation generation system identifies an animation profile for animating the virtual object displayed in an augmented reality (AR) scene. The AR animation generation system creates a link between the virtual object and the mobile computing system based upon a position of the virtual object within the AR scene and a position of a mobile device in a real-world environment. The link enables determining for each position of the mobile device in the real-world environment, a corresponding position for the virtual object in the AR scene.