公开(公告)号：US08363950B2

公开(公告)日：2013-01-29

申请号：US13426427

申请日：2012-03-21

申请人： Xinjian Chen ,  Dongmei Zhang ,  Yu Zou ,  Ming Chang ,  Shi Han ,  Jian Wang

发明人： Xinjian Chen ,  Dongmei Zhang ,  Yu Zou ,  Ming Chang ,  Shi Han ,  Jian Wang

IPC分类号： G06K9/00 ,  G06F17/00

CPC分类号： G06K9/00973 ,  G06K9/6292 ,  G06K9/6296

摘要： Described is a technology by which online recognition of handwritten input data is combined with offline recognition and processing to obtain a combined recognition result. In general, the combination improves overall recognition accuracy. In one aspect, online and offline recognition is separately performed to obtain online and offline character-level recognition scores for candidates (hypotheses). A statistical analysis-based combination algorithm, an AdaBoost algorithm, and/or a neural network-based combination may determine a combination function to combine the scores to produce a result set of one or more results. Online and offline radical-level recognition may be performed. For example, a HMM recognizer may generate online radical scores used to build a radical graph, which is then rescored using the offline radical recognition scores. Paths in the rescored graph are then searched to provide the combined recognition result, e.g., corresponding to the path with the highest score.

公开(公告)号：US08160362B2

公开(公告)日：2012-04-17

申请号：US13090242

申请日：2011-04-19

申请人： Xinjian Chen ,  Dongmei Zhang ,  Yu Zou ,  Ming Chang ,  Shi Han ,  Jian Wang

发明人： Xinjian Chen ,  Dongmei Zhang ,  Yu Zou ,  Ming Chang ,  Shi Han ,  Jian Wang

IPC分类号： G06K9/00 ,  G06F17/00

CPC分类号： G06K9/00973 ,  G06K9/6292 ,  G06K9/6296

摘要： Described is a technology by which online recognition of handwritten input data is combined with offline recognition and processing to obtain a combined recognition result. In general, the combination improves overall recognition accuracy. In one aspect, online and offline recognition is separately performed to obtain online and offline character-level recognition scores for candidates (hypotheses). A statistical analysis-based combination algorithm, an AdaBoost algorithm, and/or a neural network-based combination may determine a combination function to combine the scores to produce a result set of one or more results. Online and offline radical-level recognition may be performed. For example, a HMM recognizer may generate online radical scores used to build a radical graph, which is then rescored using the offline radical recognition scores. Paths in the rescored graph are then searched to provide the combined recognition result, e.g., corresponding to the path with the highest score.

摘要翻译： 描述了通过在线识别手写输入数据与离线识别和处理相结合以获得组合识别结果的技术。 通常，该组合提高了整体识别精度。 在一个方面，单独执行在线和离线识别以获得用于候选者（假设）的在线和离线角色级识别分数。 基于统计分析的组合算法，AdaBoost算法和/或基于神经网络的组合可以确定组合函数以组合分数以产生一个或多个结果的结果集。 可以执行在线和离线激进级别识别。 例如，HMM识别器可以生成用于构建激进图形的在线激进分数，然后使用离线激进识别分数进行重新分类。 然后，搜索折叠图中的路径以提供组合识别结果，例如对应于具有最高分数的路径。

公开(公告)号：US07953279B2

公开(公告)日：2011-05-31

申请号：US11823644

申请日：2007-06-28

申请人： Xinjian Chen ,  Dongmei Zhang ,  Yu Zou ,  Ming Chang ,  Shi Han ,  Jian Wang

发明人： Xinjian Chen ,  Dongmei Zhang ,  Yu Zou ,  Ming Chang ,  Shi Han ,  Jian Wang

IPC分类号： G06K9/00 ,  G06F17/00

CPC分类号： G06K9/00973 ,  G06K9/6292 ,  G06K9/6296

摘要： Described is a technology by which online recognition of handwritten input data is combined with offline recognition and processing to obtain a combined recognition result. In general, the combination improves overall recognition accuracy. In one aspect, online and offline recognition is separately performed to obtain online and offline character-level recognition scores for candidates (hypotheses). A statistical analysis-based combination algorithm, an AdaBoost algorithm, and/or a neural network-based combination may determine a combination function to combine the scores to produce a result set of one or more results. Online and offline radical-level recognition may be performed. For example, a HMM recognizer may generate online radical scores used to build a radical graph, which is then rescored using the offline radical recognition scores. Paths in the rescored graph are then searched to provide the combined recognition result, e.g., corresponding to the path with the highest score.

摘要翻译： 描述了通过在线识别手写输入数据与离线识别和处理相结合以获得组合识别结果的技术。 通常，该组合提高了整体识别精度。 在一个方面，单独执行在线和离线识别以获得用于候选者（假设）的在线和离线角色级识别分数。 基于统计分析的组合算法，AdaBoost算法和/或基于神经网络的组合可以确定组合函数以组合分数以产生一个或多个结果的结果集。 可以执行在线和离线激进级别识别。 例如，HMM识别器可以生成用于构建激进图形的在线激进分数，然后使用离线激进识别分数进行重新分类。 然后，搜索折叠图中的路径以提供组合识别结果，例如对应于具有最高分数的路径。

公开(公告)号：US20120183223A1

公开(公告)日：2012-07-19

申请号：US13426427

申请日：2012-03-21

申请人： Xinjian Chen ,  Dongmei Zhang ,  Yu Zou ,  Ming Chang ,  Shi Han ,  Jian Wang

发明人： Xinjian Chen ,  Dongmei Zhang ,  Yu Zou ,  Ming Chang ,  Shi Han ,  Jian Wang

IPC分类号： G06K9/62

CPC分类号： G06K9/00973 ,  G06K9/6292 ,  G06K9/6296

摘要： Described is a technology by which online recognition of handwritten input data is combined with offline recognition and processing to obtain a combined recognition result. In general, the combination improves overall recognition accuracy. In one aspect, online and offline recognition is separately performed to obtain online and offline character-level recognition scores for candidates (hypotheses). A statistical analysis-based combination algorithm, an AdaBoost algorithm, and/or a neural network-based combination may determine a combination function to combine the scores to produce a result set of one or more results. Online and offline radical-level recognition may be performed. For example, a HMM recognizer may generate online radical scores used to build a radical graph, which is then rescored using the offline radical recognition scores. Paths in the rescored graph are then searched to provide the combined recognition result, e.g., corresponding to the path with the highest score.

摘要翻译： 描述了通过在线识别手写输入数据与离线识别和处理相结合以获得组合识别结果的技术。 通常，该组合提高了整体识别精度。 在一个方面，单独执行在线和离线识别以获得用于候选者（假设）的在线和离线角色级识别分数。 基于统计分析的组合算法，AdaBoost算法和/或基于神经网络的组合可以确定组合函数以组合分数以产生一个或多个结果的结果集。 可以执行在线和离线激进级别识别。 例如，HMM识别器可以生成用于构建激进图形的在线激进分数，然后使用离线激进识别分数进行重新分类。 然后，搜索折叠图中的路径以提供组合识别结果，例如对应于具有最高分数的路径。

公开(公告)号：US20110194771A1

公开(公告)日：2011-08-11

申请号：US13090242

申请日：2011-04-19

申请人： Xinjian Chen ,  Dongmei Zhang ,  Yu Zou ,  Ming Chang ,  Shi Han ,  Jian Wang

发明人： Xinjian Chen ,  Dongmei Zhang ,  Yu Zou ,  Ming Chang ,  Shi Han ,  Jian Wang

IPC分类号： G06K9/00

CPC分类号： G06K9/00973 ,  G06K9/6292 ,  G06K9/6296

摘要： Described is a technology by which online recognition of handwritten input data is combined with offline recognition and processing to obtain a combined recognition result. In general, the combination improves overall recognition accuracy. In one aspect, online and offline recognition is separately performed to obtain online and offline character-level recognition scores for candidates (hypotheses). A statistical analysis-based combination algorithm, an AdaBoost algorithm, and/or a neural network-based combination may determine a combination function to combine the scores to produce a result set of one or more results. Online and offline radical-level recognition may be performed. For example, a HMM recognizer may generate online radical scores used to build a radical graph, which is then rescored using the offline radical recognition scores. Paths in the rescored graph are then searched to provide the combined recognition result, e.g., corresponding to the path with the highest score.

摘要翻译： 描述了通过在线识别手写输入数据与离线识别和处理相结合以获得组合识别结果的技术。 通常，该组合提高了整体识别精度。 在一个方面，单独执行在线和离线识别以获得用于候选者（假设）的在线和离线角色级识别分数。 基于统计分析的组合算法，AdaBoost算法和/或基于神经网络的组合可以确定组合函数以组合分数以产生一个或多个结果的结果集。 可以执行在线和离线激进级别识别。 例如，HMM识别器可以生成用于构建激进图形的在线激进分数，然后使用离线激进识别分数进行重新分类。 然后，搜索折叠图中的路径以提供组合识别结果，例如对应于具有最高分数的路径。

公开(公告)号：US20090003706A1

公开(公告)日：2009-01-01

申请号：US11823644

申请日：2007-06-28

申请人： Xinjian Chen ,  Dongmei Zhang ,  Yu Zou ,  Ming Chang ,  Shi Han ,  Jian Wang

发明人： Xinjian Chen ,  Dongmei Zhang ,  Yu Zou ,  Ming Chang ,  Shi Han ,  Jian Wang

IPC分类号： G06K9/00

CPC分类号： G06K9/00973 ,  G06K9/6292 ,  G06K9/6296

摘要： Described is a technology by which online recognition of handwritten input data is combined with offline recognition and processing to obtain a combined recognition result. In general, the combination improves overall recognition accuracy. In one aspect, online and offline recognition is separately performed to obtain online and offline character-level recognition scores for candidates (hypotheses). A statistical analysis-based combination algorithm, an AdaBoost algorithm, and/or a neural network-based combination may determine a combination function to combine the scores to produce a result set of one or more results. Online and offline radical-level recognition may be performed. For example, a HMM recognizer may generate online radical scores used to build a radical graph, which is then rescored using the offline radical recognition scores. Paths in the rescored graph are then searched to provide the combined recognition result, e.g., corresponding to the path with the highest score.

摘要翻译： 描述了通过在线识别手写输入数据与离线识别和处理相结合以获得组合识别结果的技术。 通常，该组合提高了整体识别精度。 在一个方面，单独执行在线和离线识别以获得用于候选者（假设）的在线和离线角色级识别分数。 基于统计分析的组合算法，AdaBoost算法和/或基于神经网络的组合可以确定组合函数以组合分数以产生一个或多个结果的结果集。 可以执行在线和离线激进级别识别。 例如，HMM识别器可以生成用于构建激进图形的在线激进分数，然后使用离线激进识别分数进行重新分类。 然后，搜索折叠图中的路径以提供组合识别结果，例如对应于具有最高分数的路径。

公开(公告)号：US20100163316A1

公开(公告)日：2010-07-01

申请号：US12345668

申请日：2008-12-30

申请人： Ming Chang ,  Shi Han ,  Dongmei Zhang ,  Yu Zou ,  Xinjian Chen

发明人： Ming Chang ,  Shi Han ,  Dongmei Zhang ,  Yu Zou ,  Xinjian Chen

IPC分类号： G08C21/00

CPC分类号： G06K9/00416 ,  G06K9/00429 ,  G16C20/70

摘要： Described is a multi-path handwriting recognition framework based upon stroke segmentation, symbol recognition, two-dimensional structure analysis and semantic structure analysis. Electronic pen input corresponding to handwritten input (e.g., a chemical expression) is recognized and output via a data structure, which may include multiple recognition candidates. A recognition framework performs stroke segmentation and symbol recognition on the input, and analyzes the structure of the input to output the data structure corresponding to recognition results. For chemical expressions, the structural analysis may perform a conditional sub-expression analysis for inorganic expressions, or organic bond detection, connection relationship analysis, organic atom determination and/or conditional sub-expression analysis for organic expressions. The structural analysis also performs subscript, superscript analysis and character determination. Further analysis may be performed, e.g., chemical valence analysis and/or semantic structure analysis.

摘要翻译： 描述了基于笔划分割，符号识别，二维结构分析和语义结构分析的多路径手写识别框架。 对应于手写输入（例如，化学表达）的电子笔输入通过可包括多个识别候选的数据结构被识别和输出。 识别框架对输入进行笔划分割和符号识别，并分析输入结构以输出与识别结果相对应的数据结构。 对于化学表达式，结构分析可以对有机表达进行无机表征或有机键检测，连接关系分析，有机原子测定和/或条件子表达分析的条件子表达分析。 结构分析还执行下标，上标分析和字符测定。 可以进行进一步分析，例如化学价态分析和/或语义结构分析。

公开(公告)号：US20080205761A1

公开(公告)日：2008-08-28

申请号：US11680566

申请日：2007-02-28

申请人： Shi Han ,  Yu Zou ,  Ming Chang ,  Peng Liu ,  Yi-Jian Wu ,  Lei Ma ,  Frank Soong ,  Dongmei Zhang ,  Jian Wang

发明人： Shi Han ,  Yu Zou ,  Ming Chang ,  Peng Liu ,  Yi-Jian Wu ,  Lei Ma ,  Frank Soong ,  Dongmei Zhang ,  Jian Wang

IPC分类号： G06K9/18

CPC分类号： G06K9/6807 ,  G06K9/468 ,  G06K2209/01 ,  G06K2209/011

摘要： Exemplary techniques are described for selecting radical sets for use in probabilistic East Asian character recognition algorithms. An exemplary technique includes applying a decomposition rule to each East Asian character of the set to generate a progressive splitting graph where the progressive splitting graph comprises radicals as nodes, formulating an optimization problem to find an optimal set of radicals to represent the set of East Asian characters using maximum likelihood and minimum description length and solving the optimization problem for the optimal set of radicals. Another exemplary technique includes selecting an optimal set of radicals by using a general function that characterizes a radical with respect to other East Asian characters and a complex function that characterizes complexity of a radical.

摘要翻译： 描述了用于选择在概率东亚字符识别算法中使用的激进集合的示例性技术。 一个示例性的技术包括将分解规则应用于集合的每个东亚字符以生成逐行分割图，其中渐进分割图包括基数作为节点，制定优化问题以找到最佳的一组基团以表示东亚集 字符使用最大似然和最小描述长度，并解决优化问题的最佳组的自由基。 另一个示例性技术包括通过使用表征相对于其他东亚字符的基数的一般函数和表征激进的复杂度的复杂函数来选择最佳的自由基集合。

公开(公告)号：US08463043B2

公开(公告)日：2013-06-11

申请号：US13526236

申请日：2012-06-18

申请人： Yu Zou ,  Ming Chang ,  Shi Han ,  Dongmei Zhang ,  Jian Wang

发明人： Yu Zou ,  Ming Chang ,  Shi Han ,  Dongmei Zhang ,  Jian Wang

IPC分类号： G06K9/00 ,  G06K9/46

CPC分类号： G06K9/00416 ,  G06K2209/011

摘要： An exemplary method for online character recognition of characters includes acquiring time sequential, online ink data for a handwritten character, conditioning the ink data to produce conditioned ink data where the conditioned ink data includes information as to writing sequence of the handwritten character and extracting features from the conditioned ink data where the features include a tangent feature, a curvature feature, a local length feature, a connection point feature and an imaginary stroke feature. Such a method may determine neighborhoods for ink data and extract features for each neighborhood. An exemplary character recognition system may use various exemplary methods for training and character recognition.

摘要翻译： 用于字符的在线字符识别的示例性方法包括获取用于手写字符的时间顺序在线墨水数据，调节墨水数据以产生经调节的墨水数据，其中经调节的墨水数据包括关于写入手写字符的序列的信息并从 调节的油墨数据，其中特征包括切线特征，曲率特征，局部长度特征，连接点特征和假想笔划特征。 这种方法可以确定墨水数据的邻域并提取每个邻域的特征。 示例性字符识别系统可以使用用于训练和字符识别的各种示例性方法。

公开(公告)号：US20110229038A1

公开(公告)日：2011-09-22

申请号：US13118045

申请日：2011-05-27

申请人： Yu Zou ,  Ming Chang ,  Shi Han ,  Dongmei Zhang ,  Jian Wang

发明人： Yu Zou ,  Ming Chang ,  Shi Han ,  Dongmei Zhang ,  Jian Wang

IPC分类号： G06K9/18

CPC分类号： G06K9/00416 ,  G06K2209/011

摘要： An exemplary method for online character recognition of East Asian characters includes acquiring time sequential, online ink data for a handwritten East Asian character, conditioning the ink data to produce conditioned ink data where the conditioned ink data includes information as to writing sequence of the handwritten East Asian character and extracting features from the conditioned ink data where the features include a tangent feature, a curvature feature, a local length feature, a connection point feature and an imaginary stroke feature. Such a method may determine neighborhoods for ink data and extract features for each neighborhood. An exemplary Hidden Markov Model based character recognition system may use various exemplary methods for training and character recognition.

摘要翻译： 用于东亚字符的在线字符识别的示例性方法包括获取用于手写东亚字符的时间顺序在线墨水数据，调节墨水数据以产生经调节的墨水数据，其中调节的墨水数据包括关于写入东方手写的顺序的信息 亚洲字符和从调节的墨水数据中提取特征，其中特征包括切线特征，曲率特征，局部长度特征，连接点特征和假想笔划特征。 这种方法可以确定墨水数据的邻域并提取每个邻域的特征。 基于示例性的基于隐马尔可夫模型的角色识别系统可以使用用于训练和角色识别的各种示例性方法。