Evernote Corporation

Natalia Galaktionova

Alexander Pashintsev

Boris Gorbatov

Eugene Livshitz

Ilia Buriak

Andrew Henderson Malcolm

Ilya Buryak

Chris O'Neill

This application is directed to recognizing unstructured information based on hints provided by structured information. A computer system obtains unstructured information collected from a handwritten or audio source, and identifies one or more terms from the unstructured information. The one or more terms includes a first term that is ambiguous. The computer system performs a recognition operation on the first term to derive a first plurality of candidate terms for the first term, and obtains first contextual information from an information template associated with the unstructured information. In accordance with the first contextual information, the computer system selects a first answer term from the first plurality of candidate terms, such that the first term is recognized as the first answer term.

Extracting structured data from handwritten and audio notes

This application is directed to vectoring a raster image in which an electronic device detects a contour of a component in the raster image, builds tangent vectors for each point of the contour and identifies a plurality of segmentation points on the contour. One or more points of sharp angle are identified on the contour in accordance with a determination that each point of sharp angle corresponds to two distinct tangent vectors and that an angle between the two distinct tangent vectors falls below a predefined threshold. A respective one of the segmentation points is positioned at each identified point of shape angle. The electronic device approximates a piecewise smooth fitting curve (e.g., a piecewise Bezier curve) having two or more fitting segments to connect the plurality of segmentation points on the contour. The piecewise smooth fitting curve is thereby provided to vectorize the raster image.

Coordinated piecewise Bezier vectorization

Vectorizing a raster image includes identifying a connectivity component in the raster image, detecting a contour of the connectivity component, building tangent vectors for each point of the contour, for each sharp angle of the contour, positioning a segmentation point of two segments at a point thereof, for each location of high curvature of the contour, positioning segments proximal thereto, composing an optimization task to approximate a piecewize Bezier curve, solving the optimization task to provide a vectorization of the raster image, and, in response to there not being a sufficient number of segments, adding additional segments. Vectorizing a raster image may also include applying perspective, color, brightness and contrast correction to the raster image and building a binary black-white representation of the raster image prior to identifying the connectivity component. The optimization task may minimize a root-mean square deviation of the piecewize Bezier curve.

Coordinated piecewise bezier vectorization

Improving visual quality of a raster image includes detecting connectivity components, detecting defects in each of the connectivity components based on a characteristic line width thereof, detecting segments in each of the connectivity components, detecting joints based on geometry of the connectivity components, creating a structural graph based on the segments and joints, and correcting the raster image according to the structural graph and detected ones of the defects. The joints may correspond to linear joints, T-joints, or X-joints. Detecting types of joints may include determining a configuration of adjacent segments in a proximity of each of the joints. A characteristic line width may be determined by determining co-boundaries on opposite sides of each of the segments and determining average distances between the co-boundaries. The raster image may be a binary black-and-white image of a line drawing obtained from a photograph or a scan of a handwritten document.

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Moscow, Russia

Natalia Galaktionova