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1. 发明授权

US5636291A Continuous parameter hidden Markov model approach to automatic handwriting recognition 失效
标题翻译：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
申请人： Eveline J. Bellegarda , Jerome R. Bellegarda , David Nahamoo , Krishna S. Nathan
发明人： Eveline J. Bellegarda , Jerome R. Bellegarda , David Nahamoo , Krishna S. Nathan
IPC分类号： G06K9/62 , G06K9/68 , G06K9/70 , G06K9/00 , G06F15/00
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。

2. 发明授权

US5343537A Statistical mixture approach to automatic handwriting recognition 失效
标题翻译：统计混合法自动手写识别
公开(公告)号：US5343537A
公开(公告)日：1994-08-30
申请号：US785642
申请日：1991-10-31
申请人： Eveline J. Bellegarda , Jerome R. Bellegarda , David Nahamoo , Krishna S. Nathan
发明人： Eveline J. Bellegarda , Jerome R. Bellegarda , David Nahamoo , Krishna S. Nathan
IPC分类号： G06K9/22 , G06K9/46 , G06K9/62 , G06K9/00
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。

3. 发明授权

US5544257A Continuous parameter hidden Markov model approach to automatic handwriting recognition 失效
标题翻译：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
申请人： Eveline J. Bellegarda , Jerome R. Bellegarda , David Nahamoo , Krishna S. Nathan
发明人： Eveline J. Bellegarda , Jerome R. Bellegarda , David Nahamoo , Krishna S. Nathan
IPC分类号： G06K9/62 , G06K9/68 , G06K9/70 , G06K9/00
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。

4. 发明授权

US5550931A Automatic handwriting recognition using both static and dynamic parameters 失效
公开(公告)号：US5550931A
公开(公告)日：1996-08-27
申请号：US450557
申请日：1995-05-25
申请人： Jerome R. Bellegarda , David Nahamoo , Krishna S. Nathan
发明人： Jerome R. Bellegarda , David Nahamoo , Krishna S. Nathan
IPC分类号： G06K9/46 , G06K9/03 , G06K9/22 , G06K9/62 , G06K9/68 , G06K9/00
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.

5. 发明授权

US5544264A Automatic handwriting recognition using both static and dynamic parameters 失效
标题翻译：使用静态和动态参数自动手写识别
公开(公告)号：US5544264A
公开(公告)日：1996-08-06
申请号：US451001
申请日：1995-05-25
申请人： Jerome R. Bellegarda , David Nahamoo , Krishna S. Nathan
发明人： Jerome R. Bellegarda , David Nahamoo , Krishna S. Nathan
IPC分类号： G06K9/46 , G06K9/03 , G06K9/22 , G06K9/62 , G06K9/68 , G06K9/00
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。

6. 发明授权

US5491758A Automatic handwriting recognition using both static and dynamic parameters 失效
公开(公告)号：US5491758A
公开(公告)日：1996-02-13
申请号：US009515
申请日：1993-01-27
申请人： Jerome R. Bellegarda , David Nahamoo , Krishna S. Nathan
发明人： Jerome R. Bellegarda , David Nahamoo , Krishna S. Nathan
IPC分类号： G06K9/46 , G06K9/03 , G06K9/22 , G06K9/62 , G06K9/68 , G06K9/00
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.

7. 发明授权

US5544261A Automatic handwriting recognition using both static and dynamic parameters 失效
公开(公告)号：US5544261A
公开(公告)日：1996-08-06
申请号：US450556
申请日：1995-05-25
申请人： Jerome R. Bellegarda , David Nahamoo , Krishna S. Nathan
发明人： Jerome R. Bellegarda , David Nahamoo , Krishna S. Nathan
IPC分类号： G06K9/46 , G06K9/03 , G06K9/22 , G06K9/62 , G06K9/68 , G06K9/00
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.

8. 发明授权

US5539839A Automatic handwriting recognition using both static and dynamic parameters 失效
公开(公告)号：US5539839A
公开(公告)日：1996-07-23
申请号：US450558
申请日：1995-05-25
申请人： Jerome R. Bellegarda , David Nahamoo , Krishna S. Nathan
发明人： Jerome R. Bellegarda , David Nahamoo , Krishna S. Nathan
IPC分类号： G06K9/46 , G06K9/03 , G06K9/22 , G06K9/62 , G06K9/68 , G06K9/00
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.

9. 发明授权

US5280562A Speech coding apparatus with single-dimension acoustic prototypes for a speech recognizer 失效
标题翻译：具有用于语音识别器的单维声学原型的语音编码装置
公开(公告)号：US5280562A
公开(公告)日：1994-01-18
申请号：US770495
申请日：1991-10-03
申请人： Lalit R. Bahl , Jerome R. Bellegarda , Edward A. Epstein , John M. Lucassen , David Nahamoo , Michael A. Picheny
发明人： Lalit R. Bahl , Jerome R. Bellegarda , Edward A. Epstein , John M. Lucassen , David Nahamoo , Michael A. Picheny
IPC分类号： G10L19/00 , G10L15/02 , G10L19/02 , H03M7/30 , G10L9/02
CPC分类号： G10L19/038 , H03M7/3082
摘要： In speech recognition and speech coding, the values of at least two features of an utterance are measured during a series of time intervals to produce a series of feature vector signals. A plurality of single-dimension prototype vector signals having only one parameter value are stored. At least two single-dimension prototype vector signals having parameter values representing first feature values, and at least two other single-dimension prototype vector signals have parameter values representing second feature values. A plurality of compound-dimension prototype vector signals have unique identification values and comprise one first-dimension and one second-dimension prototype vector signal. At least two compound-dimension prototype vector signals comprise the same first-dimension prototype vector signal. The feature values of each feature vector signal are compared to the parameter values of the compound-dimension prototype vector signals to obtain prototype match scores. The identification values of the compound-dimension prototype vector signals having the best prototype match scores for the feature vectors signals are output as a sequence of coded representations of an utterance to be recognized. A match score, comprising an estimate of the closeness of a match between a speech unit and the sequence of coded representations of the utterance, is generated for each of a plurality of speech units. At least one speech subunit, of one or more best candidate speech units having the best match scores, is displayed.
摘要翻译：在语音识别和语音编码中，在一系列时间间隔期间测量话音的至少两个特征的值，以产生一系列特征向量信号。存储仅具有一个参数值的多个单维原型矢量信号。具有表示第一特征值的参数值和至少两个其它单维原型矢量信号的至少两个单维原型矢量信号具有表示第二特征值的参数值。多个复合尺寸原型矢量信号具有唯一的识别值，并且包括一个第一维和一个第二维原型矢量信号。至少两个复合维度原型矢量信号包括相同的第一维原型矢量信号。将每个特征向量信号的特征值与化合物维度原型矢量信号的参数值进行比较，以获得原型匹配分数。具有特征矢量信号的具有最佳原型匹配分数的复合维度原型矢量信号的识别值被输出为将被识别的话语的编码表示的序列。针对多个语音单元中的每一个生成包括语音单元与语音编码表示序列之间的匹配的接近度的估计的匹配分数。显示具有最佳匹配分数的一个或多个最佳候选语音单元的至少一个语音子单元。

10. 发明授权

US5276766A Fast algorithm for deriving acoustic prototypes for automatic speech recognition 失效
标题翻译：用于自动语音识别的声学原型的快速算法
公开(公告)号：US5276766A
公开(公告)日：1994-01-04
申请号：US730714
申请日：1991-07-16
申请人： Lalit R. Bahl , Jerome R. Bellegarda , Peter V. DeSouza , David Nahamoo , Michael A. Picheny
发明人： Lalit R. Bahl , Jerome R. Bellegarda , Peter V. DeSouza , David Nahamoo , Michael A. Picheny
IPC分类号： G10L19/00 , G10L15/02 , G10L15/06 , G10L9/04
CPC分类号： G10L15/063
摘要： An apparatus for generating a set of acoustic prototype signals for encoding speech includes a memory for storing a training script model comprising a series of word-segment models. Each word-segment model comprises a series of elementary models. An acoustic measure is provided for measuring the value of at least one feature of an utterance of the training script during each of a series of time intervals to produce a series of feature vector signals representing the feature values of the utterance. An acoustic matcher is provided for estimating at least one path through the training script model which would produce the entire series of measured feature vector signals. From the estimated path, the elementary model in the training script model which would produce each feature vector signal is estimated. The apparatus further comprises a cluster processor for clustering the feature vector signals into a plurality of clusters. Each feature vector signal in a cluster corresponds to a single elementary model in a single location in a single word-segment model. Each cluster signal has a cluster value equal to an average of the feature values of all feature vectors in the signal. Finally, the apparatus includes a memory for storing a plurality of prototype vector signals. Each prototype vector signal corresponds to an elementary model, has an identifier, and comprises at least two partition values. The partition values are equal to combinations of the cluster values of one or more cluster signals corresponding to the elementary model.
摘要翻译：一种用于生成用于编码语音的声原型信号的集合的装置包括用于存储包括一系列字段模型的训练脚本模型的存储器。每个单词段模型包括一系列基本模型。提供了一种声学测量，用于在一系列时间间隔的每一个期间测量训练脚本的发音的至少一个特征的值，以产生表示发音的特征值的一系列特征向量信号。提供声学匹配器用于估计通过训练脚本模型的至少一个路径，其将产生整个测量的特征向量信号的一系列。从估计的路径，估计将产生每个特征向量信号的训练脚本模型中的基本模型。该装置还包括用于将特征向量信号聚类成多个聚类的聚类处理器。群集中的每个特征向量信号对应于单个单词段模型中单个位置中的单个基本模型。每个聚类信号具有等于信号中所有特征向量的特征值的平均值的聚类值。最后，该装置包括用于存储多个原型矢量信号的存储器。每个原型矢量信号对应于基本模型，具有标识符，并且包括至少两个分区值。分区值等于对应于基本模型的一个或多个聚类信号的聚类值的组合。

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