MATH DETECTION IN HANDWRITING
    1.
    发明公开

    公开(公告)号:EP3859602A1

    公开(公告)日:2021-08-04

    申请号:EP20305069.5

    申请日:2020-01-28

    申请人: MyScript

    IPC分类号: G06K9/00 G06K9/62 G06K9/72

    摘要: The invention relates to a method implemented by a computing device (100) for processing math and text in handwriting, comprising: identifying symbols by performing handwriting recognition on a plurality of strokes (SK); classifying, as a first classification, first symbols as either a text symbol candidate or a math symbol candidate with a confidence score reaching a first threshold; classifying, as a second classification, second symbols other than first symbols as either a text symbol candidate or a math symbol candidate with a respective confidence score by applying predefined spatial syntactic rules (RL2); updating or confirming, as a third classification, a result of the second classification by establishing semantic connections between symbols and comparing the semantic connections with the result of the second classification; and recognising each symbol as either text or math based on a result of said third classification.

    GESTURE STROKE RECOGNITION IN TOUCH-BASED USER INTERFACE INPUT

    公开(公告)号:EP4086744A1

    公开(公告)日:2022-11-09

    申请号:EP21305574.2

    申请日:2021-05-04

    申请人: MyScript

    摘要: A method for recognizing gesture strokes in user input applied onto an electronic document, comprising: receiving data based on the user input, the data representing a stroke and comprising a plurality of ink points and a plurality of timestamps associated with the plurality of ink points; segmenting the plurality of ink points into a plurality of segments each corresponding to a respective sub-stroke and comprising a respective subset of the plurality of ink points; determining a scale of the electronic document; generating a plurality of feature vectors based on the plurality of segments; normalizing a subset of the feature vectors according to the scale; and applying the plurality of feature vectors to a trained stroke classifier to generate a vector of probabilities including a probability that the stroke is a non-gesture stroke and a probability that the stroke is a given gesture stroke of a set of gesture types.