Abstract: Ink-processing technology is set forth herein for detecting a gesture that a user performs in the course of interacting with an ink document. The technology operates by identifying a grouping of ink strokes created by the user. The technology then determines whether the grouping expresses a gesture based on a combination of spatial information and image information, both of which describe the grouping. That is, the spatial information describes a sequence of positions traversed by the user in drawing the grouping of ink strokes using an ink capture device, while the image information refers to image content in an image produced by rendering the grouping into image form. The technology also provides a technique for identifying the grouping by successively expanding a region of analysis, to ultimately provide a spatial cluster of ink strokes for analysis.

Abstract: Technology is described herein for parsing an ink document having a plurality of ink strokes. The technology performs stroke-level processing on the plurality of ink strokes to produce stroke-level information, the stroke-level information identifying at least one characteristic associated with each ink stroke. The technology also performs object-level processing on individual objects within the ink document to produce object-level information, the object-level information identifying one or more groupings of ink strokes in the ink document. The technology then parses the ink document into constituent parts based on the stroke-level information and the object-level information. In some implementations, the technology converts the ink stroke data into an ink image. The stroke-level processing and/or the object-level processing may operate on the ink image using one or more neural networks.

Abstract: Methods and systems for generating a predicted occupancy grid map (OGM) over at least one future time step are described. The system include a first encoder for extracting OGM features from an input OGM in a current time step. The system also includes a recurrent neural network for generating a corrective term from at least the OGM features, wherein the corrective term represents predicted change to the input OGM, and wherein the corrective term is applied to the input OGM to generate a corrected OGM. The corrected OGM represents features corresponding to occupancy of the environment in a first future time step. The system also includes a classifier for converting the corrected OGM to the predicted OGM for the first future time step. The predicted OGM is fed back as input for performing generating a predicted OGM for a second future time step.

Abstract: Ink-processing technology is set forth herein for detecting a gesture that a user performs in the course of interacting with an ink document. The technology operates by identifying a grouping of ink strokes created by the user. The technology then determines whether the grouping expresses a gesture based on a combination of spatial information and image information, both of which describe the grouping. That is, the spatial information describes a sequence of positions traversed by the user in drawing the grouping of ink strokes using an ink capture device, while the image information refers to image content in an image produced by rendering the grouping into image form. The technology also provides a technique for identifying the grouping by successively expanding a region of analysis, to ultimately provide a spatial cluster of ink strokes for analysis.

Abstract: Technology is descried herein for parsing an ink document having a plurality of ink strokes. The technology performs stroke-level processing on the plurality of ink strokes to produce stroke-level information, the stroke-level information identifying at least one characteristic associated with each ink stroke. The technology also performs object-level processing on individual objects within the ink document to produce object-level information, the object-level information identifying one or more groupings of ink strokes in the ink document. The technology then parses the ink document into constituent parts based on the stroke-level information and the object-level information. In some implementations, the technology converts the ink stroke data into an ink image. The stroke-level processing and/or the object-level processing may operate on the ink image using one or more neural networks.

Abstract: Methods and systems for generating a predicted occupancy grid map (OGM) over at least one future time step are described. The system include a first encoder for extracting OGM features from an input OGM in a current time step. The system also includes a recurrent neural network for generating a corrective term from at least the OGM features, wherein the corrective term represents predicted change to the input OGM, and wherein the corrective term is applied to the input OGM to generate a corrected OGM. The corrected OGM represents features corresponding to occupancy of the environment in a first future time step. The system also includes a classifier for converting the corrected OGM to the predicted OGM for the first future time step. The predicted OGM is fed back as input for performing generating a predicted OGM for a second future time step.