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    SYSTEM AND METHOD FOR RECOGNIZING HANDWRITTEN STROKE INPUT
    1.
    发明公开
    SYSTEM AND METHOD FOR RECOGNIZING HANDWRITTEN STROKE INPUT 审中-公开

    公开(公告)号：EP3535652A1

    公开(公告)日：2019-09-11

    申请号：EP17791706.9

    申请日：2017-11-03

    申请人： MyScript

    发明人： ARADILLA, Guillermo ,  MANSON, Gael

    IPC分类号： G06F3/0488 ,  G06F3/023

    摘要： A system, method and computer program product for use in recognizing content associated with handwritten stroke input to a computing device is provided. The computing device is connected to an input interface. A user is able to provide input by applying pressure to or gesturing above the input interface using a finger or an instrument such as a stylus or pen. The computing device has an input management system for recognizing content defined by the input. The input management system is configured to detect input of a handwritten stroke with respect to the interactive key layout, characterize the detected handwritten stroke by a reference stroke by determining a sequence of reference points associated with a sequence of interactive keys of the interactive key layout, assign probability scores to candidate characters, and cause recognition of sequences of characters by applying a language model in accordance with the assigned probability scores.

    RECOGNIZING HANDWRITTEN TEXT BY COMBINING NEURAL NETWORKS
    2.
    发明公开
    RECOGNIZING HANDWRITTEN TEXT BY COMBINING NEURAL NETWORKS 审中-公开

    公开(公告)号：EP3910534A1

    公开(公告)日：2021-11-17

    申请号：EP20305508.2

    申请日：2020-05-15

    申请人： MyScript

    发明人： MANSON, Gaël ,  ARADILLA, Guillermo ,  CEROVIC, Cyril ,  LALLICAN, Pierre-Michel

    IPC分类号： G06K9/00 ,  G06K9/46 ,  G06K9/62

    摘要： A method for recognizing handwritten text is disclosed. The method comprises receiving data comprising a sequence of ink points; applying the received data to a neural network-based sequence classifier trained with a Connectionist Temporal Classification (CTC) output layer using forced alignment to generate an output; generating a character hypothesis as a portion of the sequence of ink points; applying the character hypothesis to a character classifier to obtain a first probability corresponding to the probability that the character hypothesis includes the given character; processing the output of the CTC output layer to determine a second probability corresponding to the probability that the given character is observed within the character hypothesis; and combining the first probability and the second probability to obtain a combined probability corresponding to the probability that the character hypothesis includes the given character.