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    • 7. 发明授权
    • Handwriting symbol recognition accuracy using speech input
    • 使用语音输入的手写符号识别精度
    • US08077975B2
    • 2011-12-13
    • US12037095
    • 2008-02-26
    • Lei MaYu ShiFrank Kao-ping Soong
    • Lei MaYu ShiFrank Kao-ping Soong
    • G06K9/00G06K9/62G06K9/72G06F3/00G10L15/26G09G5/00
    • G10L15/24G06K9/00409G06K9/00422
    • Described is a bimodal data input technology by which handwriting recognition results are combined with speech recognition results to improve overall recognition accuracy. Handwriting data and speech data corresponding to mathematical symbols are received and processed (including being recognized) into respective graphs. A fusion mechanism uses the speech graph to enhance the handwriting graph, e.g., to better distinguish between similar handwritten symbols that are often misrecognized. The graphs include nodes representing symbols, and arcs between the nodes representing probability scores. When arcs in the first and second graphs are determined to match one another, such as aligned in time and associated with corresponding symbols, the probability score in the second graph for that arc is used to adjust the matching probability score in the first graph. Normalization and smoothing may be performed to correspond the graphs to one another and to control the influence of one graph on the other.
    • 描述了一种双模数据输入技术,通过该技术,手写识别结果与语音识别结果相结合,以提高整体识别精度。 对应于数学符号的手写数据和语音数据被接收并处理(包括被识别)到各个图中。 融合机制使用语音图来增强手写图,例如更好地区分经常被误识别的类似的手写符号。 这些图包括表示符号的节点和表示概率分数的节点之间的弧。 当第一和第二图中的弧被确定为彼此匹配时,例如在时间上对齐并与对应符号相关联时,该弧的第二图中的概率分数用于调整第一图中的匹配概率得分。 可以执行归一化和平滑以将图彼此对应并且控制一个图的影响。
    • 9. 发明申请
    • HANDWRITING SYMBOL RECOGNITION ACCURACY USING SPEECH INPUT
    • 使用语音输入的手写符号识别精度
    • US20090214117A1
    • 2009-08-27
    • US12037095
    • 2008-02-26
    • Lei MaYu ShiFrank Kao-ping Soong
    • Lei MaYu ShiFrank Kao-ping Soong
    • G10L15/00
    • G10L15/24G06K9/00409G06K9/00422
    • Described is a bimodal data input technology by which handwriting recognition results are combined with speech recognition results to improve overall recognition accuracy. Handwriting data and speech data corresponding to mathematical symbols are received and processed (including being recognized) into respective graphs. A fusion mechanism uses the speech graph to enhance the handwriting graph, e.g., to better distinguish between similar handwritten symbols that are often misrecognized. The graphs include nodes representing symbols, and arcs between the nodes representing probability scores. When arcs in the first and second graphs are determined to match one another, such as aligned in time and associated with corresponding symbols, the probability score in the second graph for that arc is used to adjust the matching probability score in the first graph. Normalization and smoothing may be performed to correspond the graphs to one another and to control the influence of one graph on the other.
    • 描述了一种双模数据输入技术,通过该技术,手写识别结果与语音识别结果相结合,以提高整体识别精度。 对应于数学符号的手写数据和语音数据被接收并处理(包括被识别)到各个图中。 融合机制使用语音图来增强手写图,例如更好地区分经常被误识别的类似的手写符号。 这些图包括表示符号的节点和表示概率分数的节点之间的弧。 当第一和第二图中的弧被确定为彼此匹配时,例如在时间上对准并与对应符号相关联时,该弧的第二图中的概率分数用于调整第一图中的匹配概率分数。 可以执行归一化和平滑以将图彼此对应并且控制一个图的影响。