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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。
    • 10. 发明申请
    • Segment Sequence-Based Handwritten Expression Recognition
    • 基于片段序列的手写表达识别
    • US20100166314A1
    • 2010-07-01
    • US12346376
    • 2008-12-30
    • Yu ShiFrank Kao-Ping Soong
    • Yu ShiFrank Kao-Ping Soong
    • G06K9/00
    • G06K9/00422G06K9/6296
    • Methods and apparatuses for generating, by a computing device configured to interpret a handwritten expression, a symbol graph to represent strokes associated with the handwritten expression, are described herein. The symbol graph may include nodes, each node corresponding to a combination of a stroke and a candidate symbol for that stroke. The computing device may also generate a segment graph based on the symbol graph by combining nodes associated with a same stroke if strokes of their preceding nodes are the same. Also the computing device may perform a structure analysis on at least a subset of segment sequences represented by the segment graph to determine hypotheses for the handwritten expression. In other embodiments, rather than generate a segment graph, the computing device may determine segment sequences by selecting a number of symbol sequences from the symbol graph and combining symbol sequences having the same segmentation.
    • 本文描述了通过被配置为解读手写表达式的计算设备来生成表示与手写表达式相关联的笔画的符号图形的方法和装置。 符号图可以包括节点,每个节点对应于笔划的组合和该笔划的候选符号。 如果其前面的节点的笔划相同,则计算设备还可以通过组合与相同笔划相关联的节点来基于该符号图来生成片段图。 此外,计算设备还可以对由片段图表示的段序列的至少一个子集进行结构分析,以确定手写表达式的假设。 在其他实施例中,计算设备可以通过从符号图中选择符号序列的数目并组合具有相同分割的符号序列来确定段序列而不是生成段图。