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：US07593572B2

公开(公告)日：2009-09-22

申请号：US11315635

申请日：2006-02-09

申请人： Zhouchen Lin ,  Yantao Li ,  Yu Zou ,  Xianfang Wang ,  Jian Wang

发明人： Zhouchen Lin ,  Yantao Li ,  Yu Zou ,  Xianfang Wang ,  Jian Wang

IPC分类号： G06K9/00

CPC分类号： G06K9/00402 ,  G06K9/6277

摘要： Ink-parser-parameter optimization may be performed via parallel processing to accelerate searching for a set of optimal ink-parser parameters. Evaluators may parse pages of ink notes with different groups of parameters and may compute corresponding values for evaluation functions. Separate evaluation functions may be defined for the following types of ink-parker parsing engines: writing parser, writing/drawing classification, table detection, and list detection. A searcher may perform a grid-searching algorithm or a genetic algorithm to generate groups of parameters and may then pass the parameters to available evaluators for evaluation until evaluation-function values for a group of parameters satisfy a convergence condition.

摘要翻译： 墨水分析器参数优化可以通过并行处理来执行，以加速搜索一组最佳墨水解析器参数。 评估者可以用不同的参数组解析墨迹的页面，并可以计算评估函数的相应值。 可以为以下类型的墨水 - 停顿解析引擎定义单独的评估功能：写入解析器，写入/绘图分类，表格检测和列表检测。 搜索者可以执行网格搜索算法或遗传算法来生成参数组，然后可以将参数传递给可用的评估者进行评估，直到一组参数的评估函数值满足收敛条件为止。

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：US07050632B2

公开(公告)日：2006-05-23

申请号：US10143865

申请日：2002-05-14

申请人： Michael M. Shilman ,  Zile Wei ,  Yu Zou ,  Patrice Y. Simard ,  Sashi Raghupathy ,  F. David Jones ,  Charlton E. Lui ,  Jian Wang

发明人： Michael M. Shilman ,  Zile Wei ,  Yu Zou ,  Patrice Y. Simard ,  Sashi Raghupathy ,  F. David Jones ,  Charlton E. Lui ,  Jian Wang

IPC分类号： G06K9/18

CPC分类号： G06K9/00409 ,  G06K9/222

摘要： Electronic ink layout analysis systems and methods provide flexibility and efficiency in organizing, analyzing, and processing digital ink. These layout analysis systems and methods allow users substantial freedom in entering electronic ink into a pen-based computer system. Using these systems and methods, a user's input digital ink is not constrained by requirements that a user write in a specific screen orientation, that a user write in one specific orientation on all portions of a page, or that a user write using a specific minimum or maximum sized stroke. Rather, the systems and methods freely allow the user to write anywhere on a given page, in any orientation or size, while still enabling effective and efficient handwriting recognition and other processing of the input digital ink.

公开(公告)号：US20060078202A1

公开(公告)日：2006-04-13

申请号：US11281462

申请日：2005-11-18

申请人： Michael Shilman ,  Zile Wei ,  Yu Zou ,  Patrice Simard ,  Sashi Raghupathy ,  F. Jones ,  Charlton Lui ,  Jian Wang

发明人： Michael Shilman ,  Zile Wei ,  Yu Zou ,  Patrice Simard ,  Sashi Raghupathy ,  F. Jones ,  Charlton Lui ,  Jian Wang

IPC分类号： G06K9/18

CPC分类号： G06K9/00409 ,  G06K9/222

摘要： Electronic ink layout analysis systems and methods provide flexibility and efficiency in organizing, analyzing, and processing digital ink. These layout analysis systems and methods allow users substantial freedom in entering electronic ink into a pen-based computer system. Using these systems and methods, a user's input digital ink is not constrained by requirements that a user write in a specific screen orientation, that a user write in one specific orientation on all portions of a page, or that a user write using a specific minimum or maximum sized stroke. Rather, the systems and methods freely allow the user to write anywhere on a given page, in any orientation or size, while still enabling effective and efficient handwriting recognition and other processing of the input digital ink.

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：US20100205120A1

公开(公告)日：2010-08-12

申请号：US12366655

申请日：2009-02-06

申请人： Yu Zou ,  Hao Wei ,  Gong Cheng ,  Dongmei Zhang ,  Jian Wang

发明人： Yu Zou ,  Hao Wei ,  Gong Cheng ,  Dongmei Zhang ,  Jian Wang

IPC分类号： G06F15/18 ,  G06K9/62 ,  G10L15/00 ,  G06K9/46

CPC分类号： G06K9/6253 ,  G10L15/063

摘要： A method for researching and developing a recognition model in a computing environment, including gathering one or more data samples from one or more users in the computing environment into a training data set used for creating the recognition model, receiving one or more training parameters defining a feature extraction algorithm configured to analyze one or more features of the training data set, a classifier algorithm configured to associate the features to a template set, a selection of a subset of the training data set, a type of the data samples, or combinations thereof, creating the recognition model based on the training parameters, and evaluating the recognition model.

摘要翻译： 一种用于在计算环境中研究和开发识别模型的方法，包括将来自所述计算环境中的一个或多个用户的一个或多个数据样本收集到用于创建所述识别模型的训练数据集中，接收定义一个或多个训练参数的训练参数 特征提取算法，其被配置为分析训练数据集的一个或多个特征，分类器算法，被配置为将特征与模板集合相关联，训练数据集的子集的选择，数据样本的类型或其组合 ，基于训练参数创建识别模型，并对识别模型进行评估。

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：US20090003705A1

公开(公告)日：2009-01-01

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： G06K9/18

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。