公开(公告)号：US07974472B2

公开(公告)日：2011-07-05

申请号：US11772032

申请日：2007-06-29

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

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。

公开(公告)号：US07805004B2

公开(公告)日：2010-09-28

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： G06K9/62

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：US07561737B2

公开(公告)日：2009-07-14

申请号：US11155604

申请日：2005-06-20

申请人： Yu Zou ,  Lei Huang ,  Mingqing Xu ,  Jian-Lai Zhou ,  Yue Li ,  Dongmei Zhang ,  Xiaohui Hou ,  Wenli Zhu ,  Hongyun Yang ,  Xianfang Wang ,  Jiang Wu ,  Jian Wang

发明人： Yu Zou ,  Lei Huang ,  Mingqing Xu ,  Jian-Lai Zhou ,  Yue Li ,  Dongmei Zhang ,  Xiaohui Hou ,  Wenli Zhu ,  Hongyun Yang ,  Xianfang Wang ,  Jiang Wu ,  Jian Wang

IPC分类号： G06K9/18

CPC分类号： G06K9/222

摘要： A mechanism for recognizing and inputting handwritten mathematical expressions into a computer by providing a multi-path framework is described. The framework may include symbol grouping and recognition, tabular structure analysis, subordinate sub-expression analysis, subscript/superscript analysis and character determination, and semantic structure analysis components. A method for recognizing a handwritten mathematical expression includes receiving a plurality of input strokes corresponding to a handwritten mathematical expression and providing a candidate list of recognized candidate expressions based upon the input strokes. Input strokes are grouped into symbols, tabular structures are determined, dominant symbol candidates and subordinate symbols are determined, and subscript and superscript structures are determined.

摘要翻译： 描述了通过提供多路径框架来将手写数学表达式识别并输入到计算机中的机制。 框架可以包括符号分组和识别，表格结构分析，从属子表达分析，下标/上标分析和字符确定，以及语义结构分析组件。 用于识别手写数学表达式的方法包括接收与手写数学表达式相对应的多个输入笔画，并且基于输入的笔画提供所识别的候选表达的候选列表。 输入笔划分为符号，确定表格结构，确定主要符号候选和下级符号，并确定下标和上标结构。

公开(公告)号：US07295708B2

公开(公告)日：2007-11-13

申请号：US10850680

申请日：2004-05-20

申请人： Tongxian Chen ,  Zhouchen Lin ,  Jian Wang ,  Xianfang Wang ,  Jiang Wu ,  Yu Zou

发明人： Tongxian Chen ,  Zhouchen Lin ,  Jian Wang ,  Xianfang Wang ,  Jiang Wu ,  Yu Zou

IPC分类号： G06K9/00

CPC分类号： G06K9/00463 ,  G06F17/2241 ,  G06F17/242 ,  G06K9/222

摘要： A system and method for detection of a list in ink input is provided. A detector is provided that may detect a list such as a bulleted or numbered list of items in ink input. A group of lines may first be selected as a candidate list. Indentation level clustering and bullet detection may then be performed to determine the structure of the list. Bullet detection may be performed by detecting bullet partners, which are pairs of lines at the same indentation level that may begin with bullet candidates with similar features. The features of the bullet candidates in a pair of lines may be used to determine the likelihood of whether the pair of lines may be bullet partners. Finally, the structure of the list may be determined, including the relationship among the list items.

摘要翻译： 提供了一种用于检测墨水输入列表的系统和方法。 提供了一种检测器，其可以检测墨水输入中的诸如项目符号或编号列表的列表。 可以首先选择一组行作为候选列表。 然后可以执行缩进级聚类和子弹检测以确定列表的结构。 子弹检测可以通过检测子弹对象来执行，这些对象是相同缩进级别的线对，可以从类似特征的子弹候选开始。 可以使用一对线中的子弹候选者的特征来确定该对线是否可能是子弹对象的可能性。 最后，可以确定列表的结构，包括列表项之间的关系。

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：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：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。

公开(公告)号：US07929767B2

公开(公告)日：2011-04-19

申请号：US11155785

申请日：2005-06-20

申请人： Mingqing Xu ,  Yu Zou ,  Lei Huang ,  Jian-Lai Zhou ,  Xiaohui Hou ,  Zhouchen Lin ,  Dongmei Zhang ,  Jian Wang

发明人： Mingqing Xu ,  Yu Zou ,  Lei Huang ,  Jian-Lai Zhou ,  Xiaohui Hou ,  Zhouchen Lin ,  Dongmei Zhang ,  Jian Wang

IPC分类号： G06K9/18

CPC分类号： G06K9/00422 ,  G06K9/222 ,  G06K9/72 ,  G06K2209/01

摘要： A mechanism for recognizing and inputting handwritten mathematical expressions into a computer by providing part of a multi-path framework is described. The part of the multi-path framework includes a subordinate sub-expression analysis component. A method for analyzing a handwritten mathematical expression for a subordinate sub-expression includes identifying sub-expressions based on dominant symbols and determining a character for potential dominant symbols based upon sub-expression information. A determination may be made whether an expression structure candidate is valid and valid expression structure candidates may be stored in a parse tree.

摘要翻译： 描述了通过提供一部分多路径框架来将手写数学表达式识别并输入计算机的机制。 多路径框架的一部分包括从属子表达式分析组件。 用于分析用于下级子表达式的手写数学表达式的方法包括基于主要符号识别子表达，并且基于子表达信息确定潜在的主要符号的字符。 可以确定表达式结构候选是否是有效的，并且有效的表达式结构候选可以被存储在解析树中。

公开(公告)号：US07903877B2

公开(公告)日：2011-03-08

申请号：US11682722

申请日：2007-03-06

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： G06K9/00 ,  G06K9/18

CPC分类号： G06K9/00879

摘要： Exemplary methods, systems, and computer-readable media for developing, training and/or using models for online handwriting recognition of characters are described. An exemplary method for building a trainable radical-based HMM for use in character recognition includes defining radical nodes, where a radical node represents a structural element of an character, and defining connection nodes, where a connection node represents a spatial relationship between two or more radicals. Such a method may include determining a number of paths in the radical-based HMM using subsequence direction histogram vector (SDHV) clustering and determining a number of states in the radical-based HMM using curvature scale space-based (CSS) corner detection.

摘要翻译： 描述用于开发，训练和/或使用用于字符的在线手写识别的模型的示例性方法，系统和计算机可读介质。 用于构建用于字符识别的可训练基于激进的基于HMM的示例性方法包括定义基本节点，其中基本节点表示字符的结构元素，并且定义连接节点，其中连接节点表示两个或更多个之间的空间关系 激进分子 这种方法可以包括使用子序列方向直方图向量（SDHV）聚类确定基于激进的HMM中的路径数量，并使用基于曲率空间的（CSS）角检测确定基于激进的HMM中的状态数。

公开(公告)号：US07561738B2

公开(公告)日：2009-07-14

申请号：US11155614

申请日：2005-06-20

申请人： Yu Zou ,  Lei Huang ,  Jian-Lai Zhou ,  Mingqing Xu ,  Yue Li ,  Xiaohui Hou ,  Dongmei Zhang ,  Jian Wang

发明人： Yu Zou ,  Lei Huang ,  Jian-Lai Zhou ,  Mingqing Xu ,  Yue Li ,  Xiaohui Hou ,  Dongmei Zhang ,  Jian Wang

IPC分类号： G06K9/18

CPC分类号： G06K9/222

摘要： A mechanism for recognizing and inputting handwritten mathematical expressions into a computer by providing a part of a multi-path framework is described. The part of the multi-path framework includes a symbol grouping and recognition component that is designed to group input strokes that correspond to a handwritten mathematical expression into a symbol and to recognize the symbol based upon information associated with the grouped input strokes. A method for grouping and recognizing symbols of a handwritten mathematical expression includes receiving a plurality of input strokes corresponding to a handwritten mathematical expression, grouping the plurality of input strokes into symbols, recognizing the symbols based upon information, such as shape and time series information, associated with the grouped input strokes. Intra-group and inter-group information associated with the plurality of input strokes may be utilized to group the input strokes.

摘要翻译： 描述了通过提供多路径框架的一部分来将手写数学表达式识别并输入到计算机中的机制。 多路径框架的一部分包括符号分组和识别组件，其被设计为将对应于手写数学表达式的输入笔划分组到符号中，并且基于与分组的输入笔画相关联的信息来识别符号。 一种用于分组和识别手写数学表达符号的方法包括：接收与手写数学表达式对应的多个输入笔画，将多个输入笔划分组成符号，基于诸如形状和时间序列信息的信息识别符号， 与分组的输入笔画相关联。 可以利用与多个输入笔画相关联的组内和组间信息来对输入笔画进行分组。