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    • 3. 发明授权
    • Between-segment discontinuity reduction for text vectorization using dominant point classification
    • 使用优点分类的文本向量化的段间不连续性减少
    • US08441684B2
    • 2013-05-14
    • US12498387
    • 2009-07-07
    • Zhigang FanFrancis Kapo TseBingfeng ZhouYadong MuHe Tao
    • Zhigang FanFrancis Kapo TseBingfeng ZhouYadong MuHe Tao
    • H04N1/387G06K15/00G06K9/34
    • G06K9/4671G06K9/44G06K2209/01
    • What is disclosed is a novel system and method for text vectorization for bitmap images with reduced artificial discontinuities. Dominant points are identified in a bitmap character image. An initial curve is fitted to edge points of the character image in a vicinity of a selected dominant point. A set of boundary parameters in a vicinity of the selected dominant point are estimated based upon the initial curve. The selected dominant point is then classified as one of a sharp dominant point and a smooth dominant point based upon the boundary parameters or alternatively upon predefined classifications produced by an optical character recognition process. Curves are fitted between the selected dominant point and adjacent dominant points. The fitted curves maintain the estimated boundary parameters in the vicinity of smooth dominant points. A vectorized representation of the text character image based upon the fitted curves is produced as output.
    • 公开的是用于具有减少的人造不连续性的位图图像的文本向量化的新颖系统和方法。 主要点在位图字符图像中被识别。 初始曲线拟合到所选择的优势点附近的字符图像的边缘点。 基于初始曲线估计所选优势点附近的一组边界参数。 然后,基于边界参数或者通过光学字符识别处理产生的预定义分类,选择的主点被分类为锐利主要点和平滑主要点之一。 曲线拟合在选定的优势点和相邻优势点之间。 拟合曲线保持平滑优势点附近的估计边界参数。 作为输出,产生基于拟合曲线的文本字符图像的向量化表示。
    • 10. 发明申请
    • BETWEEN-SEGMENT DISCONTINUITY REDUCTION FOR TEXT VECTORIZATION USING DOMINANT POINT CLASSIFICATION
    • 使用主要点分类进行文本验证的分段不连续性减少
    • US20110007334A1
    • 2011-01-13
    • US12498387
    • 2009-07-07
    • Zhigang FanFrancis Kapo TseBingfeng ZhouYadong MuHe Tao
    • Zhigang FanFrancis Kapo TseBingfeng ZhouYadong MuHe Tao
    • G06K15/02
    • G06K9/4671G06K9/44G06K2209/01
    • What is disclosed is a novel system and method for text vectorization for bitmap images with reduced artificial discontinuities. Dominant points are identified in a bitmap character image. An initial curve is fitted to edge points of the character image in a vicinity of a selected dominant point. A set of boundary parameters in a vicinity of the selected dominant point are estimated based upon the initial curve. The selected dominant point is then classified as one of a sharp dominant point and a smooth dominant point based upon the boundary parameters or alternatively upon predefined classifications produced by an optical character recognition process. Curves are fitted between the selected dominant point and adjacent dominant points. The fitted curves maintain the estimated boundary parameters in the vicinity of smooth dominant points. A vectorized representation of the text character image based upon the fitted curves is produced as output.
    • 公开的是用于具有减少的人造不连续性的位图图像的文本向量化的新颖系统和方法。 主要点在位图字符图像中被识别。 初始曲线拟合到所选择的优势点附近的字符图像的边缘点。 基于初始曲线估计所选优势点附近的一组边界参数。 然后,基于边界参数或者通过光学字符识别处理产生的预定义分类,选择的主点被分类为锐利主要点和平滑主要点之一。 曲线拟合在选定的优势点和相邻优势点之间。 拟合曲线保持平滑优势点附近的估计边界参数。 作为输出,产生基于拟合曲线的文本字符图像的向量化表示。