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Abstract

Methods, systems, and computer program products are provided for stroke attribute matrices. User input strokes may be converted into attributes encoded in one or more stroke attribute matrices (SAMs), such as bitmaps, for image or other multidimensional analysis. One or more convolutional neural networks (CNNs) may recognize letters, symbols, shapes and gestures in SAMs. A selector may select output classifications from among multiple CNNs. A sequence analyzer may select a sequence of selected CNN outputs. Stroke information may comprise, for example, velocity (e.g. direction and speed), tilt, pressure, line width, pen up/down events, hover height, etc. Stroke information may be stored, for example, in bitmap color channels (e.g. to facilitate human review). For example, an x, y velocity vector and x, y tilt may be encoded, respectively, as RGBA components of pixel data. Stroke crossings may be encoded, for example, by combining attribute values at pixels where strokes intersect.

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First Claim