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1. 发明授权

US07302099B2 Stroke segmentation for template-based cursive handwriting recognition 有权
标题翻译：基于模板的草书手写识别的笔画分割
公开(公告)号：US07302099B2
公开(公告)日：2007-11-27
申请号：US10704785
申请日：2003-11-10
申请人： Qi Zhang , Henry A. Rowley , Ahmad A. Abdulkader , Angshuman Guha
发明人： Qi Zhang , Henry A. Rowley , Ahmad A. Abdulkader , Angshuman Guha
IPC分类号： G06K9/18 , G06K9/00
CPC分类号： G06K9/222
摘要： Ink strokes of cursive writing are segmented to make the cursive writing more like print writing, particularly with respect to the number of strokes of a character. A stroke-segmentation module first finds the local extrema points on a stroke of input ink. Then the local extrema points are stepped through, two (or three) at a time. The stroke-segmentation module may compare the three (or four) ink segments that are adjacent to the two (or three) local extrema points to a set of predefined stroke-segmentation patterns to find a closest matching pattern. Strokes are then segmented based on a stroke-segmentation rule that corresponds to the closest matching pattern. Additional stroke segmentation may be performed based on the change of curvature of the segmented ink strokes. Then, a character-recognition module performs character recognition processing by comparing the segmented ink strokes to prototype samples at least some of which have been similarly segmented.
摘要翻译：草书写作的墨水笔划被分割，以使草书写作更像打印写作，特别是关于字符的笔画数量。笔划分割模块首先在输入墨水的笔画上找到局部极值点。那么当地的极值点就是一步一步的，两次（或三次）。笔划分割模块可以将与两个（或三个）局部极值点相邻的三个（或四个）墨段与一组预定义的笔划分割模式进行比较，以找到最接近的匹配模式。然后基于与最接近的匹配模式对应的笔划分割规则来分割笔画。可以基于分段墨水笔画的曲率变化来执行附加笔划分割。然后，字符识别模块通过将分割的墨水笔画与其中至少一些类似地分段的原型样本进行比较来执行字符识别处理。

2. 发明授权

US07865018B2 Personalized implicit and explicit character shape adaptation and recognition 有权
标题翻译：个性化隐性和明确的字符形状适应与识别
公开(公告)号：US07865018B2
公开(公告)日：2011-01-04
申请号：US11150830
申请日：2005-06-10
申请人： Ahmad A. Abdulkader , Ioannis A. Drakopoulos , Qi Zhang
发明人： Ahmad A. Abdulkader , Ioannis A. Drakopoulos , Qi Zhang
IPC分类号： G06K9/18
CPC分类号： G06K9/00436 , G06K9/00429 , G06K2209/011
摘要： Handwriting recognition techniques employing a personalized handwriting recognition engine. The recognition techniques use examples of an individual's previous writing style to help recognize new pen input from that individual. The techniques also employ a shape trainer to select samples of an individual's handwriting that accurately represent the individual's writing style, for use as prototypes to recognize subsequent handwriting from the individual. The techniques also alternately or additionally employ an intelligent combiner to combine the recognition results from the personalized recognition engine and the conventional recognition engine (or engines). The combiner may use a comparative neural network to combine the recognition results from multiple recognition engines. The combiner alternately may use a rule-based system based on prior knowledge of different recognition engines.
摘要翻译：使用个性化手写识别引擎的手写识别技术。识别技术使用个人以前的写作风格的例子来帮助识别该个人的新笔输入。这些技术还使用形状训练器来选择准确地代表个人的写作风格的个人笔迹的样本，用作原型以识别来自个体的后续手写。这些技术还交替地或另外使用智能组合器来组合来自个性化识别引擎和常规识别引擎（或引擎）的识别结果。组合器可以使用比较神经网络来组合来自多个识别引擎的识别结果。组合器交替地可以使用基于不同识别引擎的先验知识的基于规则的系统。

3. 发明申请

US20070280536A1 Combiner for improving handwriting recognition 有权
标题翻译：组合器，用于改善手写识别
公开(公告)号：US20070280536A1
公开(公告)日：2007-12-06
申请号：US11443762
申请日：2006-05-31
申请人： Qi Zhang , Ahmad A. Abdulkader , Michael T. Black
发明人： Qi Zhang , Ahmad A. Abdulkader , Michael T. Black
IPC分类号： G06K9/00 , G06K9/62
CPC分类号： G06K9/00422 , G06K9/6256 , G06K9/6292
摘要： Various technologies and techniques are disclosed that improve handwriting recognition operations. Handwritten input is received in training mode and run through several base recognizers to generate several alternate lists. The alternate lists are unioned together into a combined alternate list. If the correct result is in the combined list, each correct/incorrect alternate pair is used to generate training patterns. The weights associated with the alternate pairs are stored. At runtime, the combined alternate list is generated just as training time. The trained comparator-net can be used to compare any two alternates in the combined list. A template matching base recognizer is used with one or more neural network base recognizers to improve recognition operations. The system provides comparator-net and reorder-net processes trained on print and cursive data, and ones that have been trained on cursive-only data. The respective comparator-net and reorder-net processes are used accordingly.
摘要翻译：公开了改进手写识别操作的各种技术和技术。手写输入以训练模式接收，并通过几个基本识别器生成多个替代列表。备用列表一起组合成组合的备用列表。如果正确的结果在组合列表中，则使用每个正确/不正确的备用对来生成训练模式。存储与替代对相关联的权重。在运行时，组合的备用列表就像培训时间一样生成。训练后的比较网可用于比较组合列表中的任何两个替代项。与一个或多个神经网络基础识别器一起使用模板匹配基础识别器来改善识别操作。该系统提供了对打印和草书数据进行培训的比较器网络和重新排序网络过程，以及已经对草书数据进行了培训的系统。相应地使用相应的比较器网络和重新排序网络进程。

4. 发明申请

US20110007963A1 COMBINER FOR IMPROVING HANDWRITING RECOGNITION 有权
标题翻译：用于改进手写识别的组合器
公开(公告)号：US20110007963A1
公开(公告)日：2011-01-13
申请号：US12880121
申请日：2010-09-12
申请人： Qi Zhang , Ahmad A. Abdulkader , Michael T. Black
发明人： Qi Zhang , Ahmad A. Abdulkader , Michael T. Black
IPC分类号： G06K9/62
CPC分类号： G06K9/00422 , G06K9/6256 , G06K9/6292
摘要： Various technologies and techniques are disclosed that improve handwriting recognition operations. Handwritten input is received in training mode and run through several base recognizers to generate several alternate lists. The alternate lists are unioned together into a combined alternate list. If the correct result is in the combined list, each correct/incorrect alternate pair is used to generate training patterns. The weights associated with the alternate pairs are stored. At runtime, the combined alternate list is generated just as training time. The trained comparator-net can be used to compare any two alternates in the combined list. A template matching base recognizer is used with one or more neural network base recognizers to improve recognition operations. The system provides comparator-net and reorder-net processes trained on print and cursive data, and ones that have been trained on cursive-only data. The respective comparator-net and reorder-net processes are used accordingly.
摘要翻译：公开了改进手写识别操作的各种技术和技术。手写输入以训练模式接收，并通过几个基本识别器生成多个替代列表。备用列表一起组合成组合的备用列表。如果正确的结果在组合列表中，则使用每个正确/不正确的备用对来生成训练模式。存储与替代对相关联的权重。在运行时，组合的备用列表就像培训时间一样生成。训练后的比较网可用于比较组合列表中的任何两个替代项。与一个或多个神经网络基础识别器一起使用模板匹配基础识别器来改善识别操作。该系统提供了对打印和草书数据进行培训的比较器网络和重新排序网络过程，以及已经对草书数据进行了培训的系统。相应地使用相应的比较器网络和重新排序网络进程。

5. 发明授权

US07817857B2 Combiner for improving handwriting recognition 有权
标题翻译：组合器，用于改善手写识别
公开(公告)号：US07817857B2
公开(公告)日：2010-10-19
申请号：US11443762
申请日：2006-05-31
申请人： Qi Zhang , Ahmad A. Abdulkader , Michael T. Black
发明人： Qi Zhang , Ahmad A. Abdulkader , Michael T. Black
IPC分类号： G06E1/00 , G06E3/00 , G06F15/18 , G06G7/00 , G06N3/02 , G06N3/08
CPC分类号： G06K9/00422 , G06K9/6256 , G06K9/6292
摘要： Various technologies and techniques are disclosed that improve handwriting recognition operations. Handwritten input is received in training mode and run through several base recognizers to generate several alternate lists. The alternate lists are unioned together into a combined alternate list. If the correct result is in the combined list, each correct/incorrect alternate pair is used to generate training patterns. The weights associated with the alternate pairs are stored. At runtime, the combined alternate list is generated just as training time. The trained comparator-net can be used to compare any two alternates in the combined list. A template matching base recognizer is used with one or more neural network base recognizers to improve recognition operations. The system provides comparator-net and reorder-net processes trained on print and cursive data, and ones that have been trained on cursive-only data. The respective comparator-net and reorder-net processes are used accordingly.
摘要翻译：公开了改进手写识别操作的各种技术和技术。手写输入以训练模式接收，并通过几个基本识别器生成多个替代列表。备用列表一起组合成组合的备用列表。如果正确的结果在组合列表中，则使用每个正确/不正确的备用对来生成训练模式。存储与替代对相关联的权重。在运行时，组合的备用列表就像培训时间一样生成。训练后的比较网可用于比较组合列表中的任何两个替代项。与一个或多个神经网络基础识别器一起使用模板匹配基础识别器来改善识别操作。该系统提供了对打印和草书数据进行培训的比较器网络和重新排序网络过程，以及已经对草书数据进行了培训的系统。相应地使用相应的比较器网络和重新排序网络进程。

6. 发明授权

US07496547B2 Handwriting recognition using a comparative neural network 失效
标题翻译：使用比较神经网络的手写识别
公开(公告)号：US07496547B2
公开(公告)日：2009-02-24
申请号：US11150852
申请日：2005-06-10
申请人： Ahmad A. Abdulkader , Ioannis A. Drakopoulos , Qi Zhang
发明人： Ahmad A. Abdulkader , Ioannis A. Drakopoulos , Qi Zhang
IPC分类号： G06F15/18
CPC分类号： G06N3/0454 , G06K9/00429 , G06K9/00436 , G06K9/6254 , G06K9/6255 , G06K9/80 , G06K2209/011
摘要： Handwriting recognition techniques employing a personalized handwriting recognition engine. The recognition techniques use examples of an individual's previous writing style to help recognize new pen input from that individual. The techniques also employ a shape trainer to select samples of an individual's handwriting that accurately represent the individual's writing style, for use as prototypes to recognize subsequent handwriting from the individual. The techniques also alternately or additionally employ an intelligent combiner to combine the recognition results from the personalized recognition engine and the conventional recognition engine (or engines). The combiner may use a comparative neural network to combine the recognition results from multiple recognition engines. The combiner alternately may use a rule-based system based on prior knowledge of different recognition engines.
摘要翻译：使用个性化手写识别引擎的手写识别技术。识别技术使用个人以前的写作风格的例子来帮助识别该个人的新笔输入。这些技术还使用形状训练器来选择准确地代表个人的写作风格的个人笔迹的样本，用作原型以识别来自个体的后续手写。这些技术还交替地或另外使用智能组合器来组合来自个性化识别引擎和常规识别引擎（或引擎）的识别结果。组合器可以使用比较神经网络来组合来自多个识别引擎的识别结果。组合器交替地可以使用基于不同识别引擎的先验知识的基于规则的系统。

7. 发明授权

US08326040B2 Combiner for improving handwriting recognition 有权
标题翻译：组合器，用于改善手写识别
公开(公告)号：US08326040B2
公开(公告)日：2012-12-04
申请号：US12880121
申请日：2010-09-12
申请人： Qi Zhang , Ahmad A. Abdulkader , Michael T. Black
发明人： Qi Zhang , Ahmad A. Abdulkader , Michael T. Black
IPC分类号： G06K9/18 , G06K9/00 , G06K9/62 , G06K9/68
CPC分类号： G06K9/00422 , G06K9/6256 , G06K9/6292
摘要： Various technologies and techniques are disclosed that improve handwriting recognition operations. Handwritten input is received in training mode and run through several base recognizers to generate several alternate lists. The alternate lists are unioned together into a combined alternate list. If the correct result is in the combined list, each correct/incorrect alternate pair is used to generate training patterns. The weights associated with the alternate pairs are stored. At runtime, the combined alternate list is generated just as training time. The trained comparator-net can be used to compare any two alternates in the combined list. A template matching base recognizer is used with one or more neural network base recognizers to improve recognition operations. The system provides comparator-net and reorder-net processes trained on print and cursive data, and ones that have been trained on cursive-only data. The respective comparator-net and reorder-net processes are used accordingly.
摘要翻译：公开了改进手写识别操作的各种技术和技术。手写输入以训练模式接收，并通过几个基本识别器生成多个替代列表。备用列表一起组合成组合的备用列表。如果正确的结果在组合列表中，则使用每个正确/不正确的备用对来生成训练模式。存储与替代对相关联的权重。在运行时，组合的备用列表就像培训时间一样生成。训练后的比较网可用于比较组合列表中的任何两个替代项。与一个或多个神经网络基础识别器一起使用模板匹配基础识别器来改善识别操作。该系统提供了对打印和草书数据进行培训的比较器网络和重新排序网络过程，以及已经对草书数据进行了培训的系统。相应地使用相应的比较器网络和重新排序网络进程。

8. 发明授权

US07369702B2 Template-based cursive handwriting recognition 失效
标题翻译：基于模板的草书手写识别
公开(公告)号：US07369702B2
公开(公告)日：2008-05-06
申请号：US10702663
申请日：2003-11-07
申请人： Ahmad A. Abdulkader , Brian Leung , Henry Allan Rowley , Qi Zhang
发明人： Ahmad A. Abdulkader , Brian Leung , Henry Allan Rowley , Qi Zhang
IPC分类号： G06K9/00 , G06K9/62
CPC分类号： G06K9/6835
摘要： Input handwritten characters are classified as print or cursive based upon numerical feature values calculated from the shape of an input character. The feature values are applied to inputs of an artificial neural network which outputs a probability of the input character being print or cursive. If a character is classified as print, it is analyzed by a print character recognizer. If a character is classified as cursive, it is analyzed using a cursive character recognizer. The cursive character recognizer compares the input character to multiple prototype characters using a Dynamic Time Warping (DTW) algorithm.
摘要翻译：基于从输入字符的形状计算的数值特征值，输入手写字符被分类为打印或草书。特征值被应用于人造神经网络的输入，其输出输入字符是打印或草书的概率。如果字符被分类为打印，则由打印字符识别器进行分析。如果角色被分类为草书，则使用草书字符识别器进行分析。草书字符识别器使用动态时间扭曲（DTW）算法将输入字符与多个原型字符进行比较。

9. 发明申请

US20050100214A1 Stroke segmentation for template-based cursive handwriting recognition 有权
标题翻译：基于模板的草书手写识别的笔画分割
公开(公告)号：US20050100214A1
公开(公告)日：2005-05-12
申请号：US10704785
申请日：2003-11-10
申请人： Qi Zhang , Henry Rowley , Ahmad Abdulkader , Angshuman Guha
发明人： Qi Zhang , Henry Rowley , Ahmad Abdulkader , Angshuman Guha
IPC分类号： G06K9/18 , G06K9/22 , G06K9/34
CPC分类号： G06K9/222
摘要： Ink strokes of cursive writing are segmented to make the cursive writing more like print writing, particularly with respect to the number of strokes of a character. A stroke-segmentation module first finds the local extrema points on a stroke of input ink. Then the local extrema points are stepped through, two (or three) at a time. The stroke-segmentation module may compare the three (or four) ink segments that are adjacent to the two (or three) local extrema points to a set of predefined stroke-segmentation patterns to find a closest matching pattern. Strokes are then segmented based on a stroke-segmentation rule that corresponds to the closest matching pattern. Additional stroke segmentation may be performed based on the change of curvature of the segmented ink strokes. Then, a character-recognition module performs character recognition processing by comparing the segmented ink strokes to prototype samples at least some of which have been similarly segmented.
摘要翻译：草书写作的墨水笔划被分割，以使草书写作更像打印写作，特别是关于字符的笔画数量。笔划分割模块首先在输入墨水的笔画上找到局部极值点。那么当地的极值点就是一步一步的，两次（或三次）。笔划分割模块可以将与两个（或三个）局部极值点相邻的三个（或四个）墨段与一组预定义的笔划分割模式进行比较，以找到最接近的匹配模式。然后基于与最接近的匹配模式对应的笔划分割规则来分割笔画。可以基于分段墨水笔画的曲率变化来执行附加笔划分割。然后，字符识别模块通过将分割的墨水笔画与其中至少一些类似地分段的原型样本进行比较来执行字符识别处理。

10. 外观设计

USD1041157S1 Lanyard 有权
公开(公告)号：USD1041157S1
公开(公告)日：2024-09-10
申请号：US29934197
申请日：2024-03-25
申请人： Qi Zhang
设计人： Qi Zhang
摘要： The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.
FIG. 1 is a front perspective view of a lanyard showing the new design;
FIG. 2 is a back perspective view thereof;
FIG. 3 is a left-side perspective view thereof;
FIG. 4 is a right-side perspective view thereof,
FIG. 5 is a top view thereof;
FIG. 6 is a bottom view thereof;
FIG. 7 is an enlarged partial view thereof;
FIG. 8 is another enlarged partial view thereof;
FIG. 9 is another perspective view thereof; and,
FIG. 10 is another perspective view thereof.

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