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公开(公告)号：US5550931A
公开(公告)日：1996-08-27
申请号：US450557
申请日：1995-05-25
CPC分类号： G06K9/6293 , G06K9/00416 , G06K9/00429
摘要： Methods and apparatus are disclosed for recognizing handwritten characters in response to an input signal from a handwriting transducer. A feature extraction and reduction procedure is disclosed that relies on static or shape information, wherein the temporal order in which points are captured by an electronic tablet may be disregarded. A method of the invention generates and processes the tablet data with three independent sets of feature vectors which encode the shape information of the input character information. These feature vectors include horizontal (x-axis) and vertical (y-axis) slices of a bit-mapped image of the input character data, and an additional feature vector to encode an absolute y-axis displacement from a baseline of the bit-mapped image. It is shown that the recognition errors that result from the spatial or static processing are quite different from those resulting from temporal or dynamic processing. Furthermore, it is shown that these differences complement one another. As a result, a combination of these two sources of feature vector information provides a substantial reduction in an overall recognition error rate. Methods to combine probability scores from dynamic and the static character models are also disclosed.
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2.发明授权Continuous parameter hidden Markov model approach to automatic handwriting recognition 失效连续参数隐马尔可夫模型法自动手写识别
公开(公告)号：US5636291A
公开(公告)日：1997-06-03
申请号：US467615
申请日：1995-06-06
CPC分类号： G06K9/6297
摘要： A computer-based system and method for recognizing handwriting. The present invention includes a pre-processor, a front end, and a modeling component. The present invention operates as follows. First, the present invention identifies the lexemes for all characters of interest. Second, the present invention performs a training phase in order to generate a hidden Markov model for each of the lexemes. Third, the present invention performs a decoding phase to recognize handwritten text. Hidden Markov models for lexemes are produced during the training phase. The present invention performs the decoding phase as follows. The present invention receives test characters to be decoded (that is, to be recognized). The present invention generates sequences of feature vectors for the test characters by mapping in chirographic space. For each of the test characters, the present invention computes probabilities that the test character can be generated by the hidden Markov models. The present invention decodes the test character as the recognized character associated with the hidden Markov model having the greatest probability.
摘要翻译： 一种用于识别笔迹的基于计算机的系统和方法。 本发明包括预处理器，前端和建模组件。 本发明如下操作。 首先，本发明识别所有感兴趣的人物的词汇。 第二，本发明执行训练阶段，以便为每个词汇生成隐马尔可夫模型。 第三，本发明执行解码阶段来识别手写文本。 训练阶段产生了隐马尔可夫模型。 本发明如下进行解码阶段。 本发明接收要解码的测试字符（即将被识别）。 本发明通过在手写空间中映射来生成用于测试字符的特征向量的序列。 对于每个测试字符，本发明计算由隐马尔可夫模型可以产生测试字符的概率。 本发明将测试字符解码为与具有最大概率的隐马尔可夫模型相关联的识别字符。
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公开(公告)号：US5343537A
公开(公告)日：1994-08-30
申请号：US785642
申请日：1991-10-31
CPC分类号： G06K9/6217 , G06K9/00416 , G06K9/00429
摘要： Method and apparatus for automatic recognition of handwritten text based on a suitable representation of handwriting in one or several feature vector spaces(s), Gaussian modeling in each space, and mixture decoding to take into account the contribution of all relevant prototypes in all spaces. The feature vector space(s) is selected to encompass both a local and a global description of each appropriate point on a pen trajectory. Windowing is performed to capture broad trends in the handwriting, after which a linear transformation is applied to suitably eliminate redundancy. The resulting feature vector space(s) is called chirographic space(s). Gaussian modeling is performed to isolate adequate chirographic prototype distributions in each space, and the mixture coefficients weighting these distributions are trained using a maximum likelihood framework. Decoding can be performed simply and effectively by accumulating the contribution of all relevant prototype distributions. Post-processing using a language model may be included.
摘要翻译： 基于在一个或多个特征向量空间中的手写的适当表示，每个空间中的高斯建模，以及混合解码，以便考虑所有空间中所有相关原型的贡献，自动识别手写文本的方法和装置。 选择特征向量空间以包含笔轨迹上的每个适当点的局部和全局描述。 执行窗口以捕获手写的广泛趋势，之后应用线性变换以适当地消除冗余。 所得到的特征向量空间称为手绘空间。 执行高斯建模以分离每个空间中的足够的手写原型分布，并且使用最大似然框架训练对这些分布加权的混合系数。 通过积累所有相关原型分布的贡献，可以简单有效地执行解码。 可以包括使用语言模型的后处理。
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4.发明授权Continuous parameter hidden Markov model approach to automatic handwriting recognition 失效连续参数隐马尔可夫模型法自动手写识别
公开(公告)号：US5544257A
公开(公告)日：1996-08-06
申请号：US818193
申请日：1992-01-08
CPC分类号： G06K9/6297
摘要： A computer-based system and method for recognizing handwriting. The present invention includes a preprocessor, a front end, and a modeling component. The present invention operates as follows. First, the present invention identifies the lexemes for all characters of interest. Second, the present invention performs a training phase in order to generate a hidden Markov model for each of the lexemes. Third, the present invention performs a decoding phase to recognize handwritten text. Hidden Markov models for lexemes are produced during the training phase. The present invention performs the decoding phase as follows. The present invention receives test characters to be decoded (that is, to be recognized). The present invention generates sequences of feature vectors for the test characters by mapping in chirographic space. For each of the test characters, the present invention computes probabilities that the test character can be generated by the hidden Markov models. The present invention decodes the test character as the recognized character associated with the hidden Markov model having the greatest probability.
摘要翻译： 一种用于识别笔迹的基于计算机的系统和方法。 本发明包括预处理器，前端和建模部件。 本发明如下操作。 首先，本发明识别所有感兴趣的人物的词汇。 第二，本发明执行训练阶段，以便为每个词汇生成隐马尔可夫模型。 第三，本发明执行解码阶段来识别手写文本。 训练阶段产生了隐马尔可夫模型。 本发明如下进行解码阶段。 本发明接收要解码的测试字符（即将被识别）。 本发明通过在手写空间中映射来生成用于测试字符的特征向量的序列。 对于每个测试字符，本发明计算由隐马尔可夫模型可以产生测试字符的概率。 本发明将测试字符解码为与具有最大概率的隐马尔可夫模型相关联的识别字符。
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5.发明授权
公开(公告)号：US5544264A
公开(公告)日：1996-08-06
申请号：US451001
申请日：1995-05-25
CPC分类号： G06K9/6293 , G06K9/00416 , G06K9/00429
摘要： Methods and apparatus are disclosed for recognizing handwritten characters in response to an input signal from a handwriting transducer. A feature extraction and reduction procedure is disclosed that relies on static or shape information, wherein the temporal order in which points are captured by an electronic tablet may be disregarded. A method of the invention generates and processes the tablet data with three independent sets of feature vectors which encode the shape information of the input character information. These feature vectors include horizontal (x-axis) and vertical (y-axis) slices of a bit-mapped image of the input character data, and an additional feature vector to encode an absolute y-axis displacement from a baseline of the bit-mapped image. It is shown that the recognition errors that result from the spatial or static processing are quite different from those resulting from temporal or dynamic processing. Furthermore, it is shown that these differences complement one another. As a result, a combination of these two sources of feature vector information provides a substantial reduction in an overall recognition error rate. Methods to combine probability scores from dynamic and the static character models are also disclosed.
摘要翻译： 公开了用于响应于来自手写传感器的输入信号识别手写字符的方法和装置。 公开了一种依赖于静态或形状信息的特征提取和缩减过程，其中可以忽略由电子平板电脑捕获点的时间顺序。 本发明的方法利用编码输入字符信息的形状信息的三个独立的特征向量组来生成和处理图形输入板数据。 这些特征向量包括输入字符数据的位映射图像的水平（x轴）和垂直（y轴）切片，以及附加特征向量，用于编码从比特映射图像的基线的绝对y轴位移。 映射图像。 显示由空间或静态处理产生的识别错误与由时间或动态处理产生的识别错误截然不同。 此外，这表明这些差异相互补充。 结果，这两个特征向量信息源的组合提供了总体识别错误率的显着降低。 还公开了从动态和静态字符模型组合概率分数的方法。
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公开(公告)号：US5491758A
公开(公告)日：1996-02-13
申请号：US009515
申请日：1993-01-27
CPC分类号： G06K9/6293 , G06K9/00416 , G06K9/00429
摘要： Methods and apparatus are disclosed for recognizing handwritten characters in response to an input signal from a handwriting transducer. A feature extraction and reduction procedure is disclosed that relies on static or shape information, wherein the temporal order in which points are captured by an electronic tablet may be disregarded. A method of the invention generates and processes the tablet data with three independent sets of feature vectors which encode the shape information of the input character information. These feature vectors include horizontal (x-axis) and vertical (y-axis) slices of a bit-mapped image of the input character data, and an additional feature vector to encode an absolute y-axis displacement from a baseline of the bit-mapped image. It is shown that the recognition errors that result from the spatial or static processing are quite different from those resulting from temporal or dynamic processing. Furthermore, it is shown that these differences complement one another. As a result, a combination of these two sources of feature vector information provides a substantial reduction in an overall recognition error rate. Methods to combine probability scores from dynamic and the static character models are also disclosed.
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公开(公告)号：US5544261A
公开(公告)日：1996-08-06
申请号：US450556
申请日：1995-05-25
CPC分类号： G06K9/6293 , G06K9/00416 , G06K9/00429
摘要： Methods and apparatus are disclosed for recognizing handwritten characters in response to an input signal from a handwriting transducer. A feature extraction and reduction procedure is disclosed that relies on static or shape information, wherein the temporal order in which points are captured by an electronic tablet may be disregarded. A method of the invention generates and processes the tablet data with three independent sets of feature vectors which encode the shape information of the input character information. These feature vectors include horizontal (x-axis) and vertical (y-axis) slices of a bit-mapped image of the input character data, and an additional feature vector to encode an absolute y-axis displacement from a baseline of the bit-mapped image. It is shown that the recognition errors that result from the spatial or static processing are quite different from those resulting from temporal or dynamic processing. Furthermore, it is shown that these differences complement one another. As a result, a combination of these two sources of feature vector information provides a substantial reduction in an overall recognition error rate. Methods to combine probability scores from dynamic and the static character models are also disclosed.
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公开(公告)号：US5539839A
公开(公告)日：1996-07-23
申请号：US450558
申请日：1995-05-25
CPC分类号： G06K9/6293 , G06K9/00416 , G06K9/00429
摘要： Methods and apparatus are disclosed for recognizing handwritten characters in response to an input signal from a handwriting transducer. A feature extraction and reduction procedure is disclosed that relies on static or shape information, wherein the temporal order in which points are captured by an electronic tablet may be disregarded. A method of the invention generates and processes the tablet data with three independent sets of feature vectors which encode the shape information of the input character information. These feature vectors include horizontal (x-axis) and vertical (y-axis) slices of a bit-mapped image of the input character data, and an additional feature vector to encode an absolute y-axis displacement from a baseline of the bit-mapped image. It is shown that the recognition errors that result from the spatial or static processing are quite different from those resulting from temporal or dynamic processing. Furthermore, it is shown that these differences complement one another. As a result, a combination of these two sources of feature vector information provides a substantial reduction in an overall recognition error rate. Methods to combine probability scores from dynamic and the static character models are also disclosed.
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9.发明授权System and method for displaying page information in a personal digital notepad 有权在个人数字记事本中显示页面信息的系统和方法公开(公告)号：US06326957B1
公开(公告)日：2001-12-04
申请号：US09240213
申请日：1999-01-29
申请人： Krishna S. Nathan , Michael P. Perrone , John F. Pitrelli , Eugene H. Ratzlaff , Jayashree Subrahmonia
发明人： Krishna S. Nathan , Michael P. Perrone , John F. Pitrelli , Eugene H. Ratzlaff , Jayashree Subrahmonia
IPC分类号： G09G500
CPC分类号： G06F3/04883 , G06F3/0488
摘要： System and methods for visually displaying page information in a handwriting recording device such as a personal digital notepad (PDN) device, in which constraints exist which limit the size of a user interface display (e.g. LCD). Various methods allow a user to view detailed page information by selecting one or more available display modes which display the selected information using one or more dynamic icons. In addition, the user can view (via the display) selected portions of handwriting content of a given electronic page, thereby affording the user the opportunity to synchronize the stored handwriting data with the handwritten text.
摘要翻译： 在诸如个人数字记事本（PDN）设备的手写记录设备中可视地显示页面信息的系统和方法，其中存在限制用户界面显示（例如LCD）的大小的约束。 各种方法允许用户通过选择使用一个或多个动态图标显示所选信息的一个或多个可用显示模式来查看详细的页面信息。 此外，用户可以（通过显示器）查看给定电子页面的手写内容的所选部分，从而为用户提供将存储的笔迹数据与手写文本同步的机会。
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10.发明授权Methods and apparatus for customizing handwriting models to individual writers 失效将手写模型定制到个别作家的方法和设备公开(公告)号：US06256410B1
公开(公告)日：2001-07-03
申请号：US09126251
申请日：1998-07-30
IPC分类号： G06K900
CPC分类号： G06K9/00879
摘要： A method of training a writer dependent handwriting recognition system with handwriting samples of a specific writer comprises the steps of: capturing the handwriting samples of the specific writer; segmenting the handwriting samples of the specific writer; initializing handwriting models associated with the specific writer from the segmented handwriting samples; and refining the initialized handwriting models associated with the specific writer to generate writer dependent handwriting models for use by the writer dependent handwriting recognition system. Preferably, the method also comprises the step of repeating the refining step until the writer dependent handwriting models yield recognition results substantially satisfying a predetermined accuracy threshold.
摘要翻译： 一种使用具有特定作者的笔迹样本的写入者依赖手写识别系统进行训练的方法包括以下步骤：捕获特定作者的手写样本; 分割具体作者的笔迹样本; 从分段手写样本初始化与特定作者相关联的手写模型; 并且改进与特定写入器相关联的初始化手写模型，以产生写入者依赖手写模型供作者依赖手写识别系统使用。 优选地，该方法还包括重复精炼步骤的步骤，直到写入者依赖手写模型产生基本上满足预定精度阈值的识别结果。
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