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公开(公告)号:US20240265719A1
公开(公告)日:2024-08-08
申请号:US18165125
申请日:2023-02-06
发明人: Yuan Yuan DING , Zhong Fang YUAN , Tong LIU , Si Tong ZHAO , Yi Chen ZHONG
IPC分类号: G06V30/19 , G06V10/82 , G06V30/148 , G06V30/414
CPC分类号: G06V30/1914 , G06V10/82 , G06V30/148 , G06V30/19007 , G06V30/414
摘要: Embodiments of the present disclosure provide systems and methods for implementing enhanced Optical Character Recognition (OCR) of text overlapping scenes through text graph structuring. Text graph structuring is performed to provide a graph data structure for each data character or letter of multiple letters and a library of graph templates from graph structured data of each of the multiple letters. Text graph structuring is performed to convert visual content of an identified overlapping text image region to an overlapping text topology graph. The overlapping text topology graph is split into multiple subgraphs using the graph template library to match recognizable letters in the overlapping text.
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公开(公告)号:US20230334318A1
公开(公告)日:2023-10-19
申请号:US18341050
申请日:2023-06-26
IPC分类号: G06N3/08 , G06F18/2323 , G06F18/2415 , G06F18/2431 , G06V30/19 , G06V10/772 , G06V10/774 , G06V10/80 , G06F18/2321 , G06F18/25 , G06N3/045 , G06N3/04 , G06N5/022 , G06N5/025
CPC分类号: G06N3/08 , G06F18/2323 , G06F18/2415 , G06F18/2431 , G06V30/19107 , G06V30/1914 , G06V30/19147 , G06V30/1918 , G06V10/772 , G06V10/774 , G06V10/806 , G06V10/809 , G06F18/2321 , G06F18/253 , G06F18/254 , G06N3/045 , G06N3/04 , G06N5/022 , G06N5/025
摘要: A method and system for training a neural network are provided. The method includes receiving an input image, selecting at least one data augmentation method from a pool of data augmentation methods, generating an augmented image by applying the selected at least one data augmentation method to the input image, and generating a mixed image from the input image and the augmented image.
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公开(公告)号:US11699278B2
公开(公告)日:2023-07-11
申请号:US17397297
申请日:2021-08-09
发明人: Ming-Jung Seow , Gang Xu , Tao Yang , Wesley K. Cobb
IPC分类号: G06V10/32 , G06F16/23 , G06F16/28 , G06V30/262 , H01B1/02 , G06F18/23 , G06F18/28 , G06F18/2137 , G06N7/01 , G06V30/19 , G06V10/762
CPC分类号: G06V10/32 , G06F18/2137 , G06F18/23 , G06F18/28 , G06N7/01 , G06V10/762 , G06V30/1914 , G06V30/19127 , G06V30/268 , H01B1/02
摘要: Techniques are disclosed for generating a sequence of symbols based on input data for a neuro-linguistic model. The model may be used by a behavior recognition system to analyze the input data. A mapper component of a neuro-linguistic module in the behavior recognition system receives one or more normalized vectors generated from the input data. The mapper component generates one or more clusters based on a statistical distribution of the normalized vectors. The mapper component evaluates statistics and identifies statistically relevant clusters. The mapper component assigns a distinct symbol to each of the identified clusters.
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公开(公告)号:US11687780B2
公开(公告)日:2023-06-27
申请号:US17241848
申请日:2021-04-27
IPC分类号: G06K9/62 , G06N3/08 , G06F18/2323 , G06F18/2415 , G06F18/2431 , G06V30/19 , G06V10/772 , G06V10/774 , G06V10/80 , G06F18/2321 , G06F18/25 , G06N3/045 , G06N3/04 , G06N5/022 , G06N5/025
CPC分类号: G06N3/08 , G06F18/2321 , G06F18/2323 , G06F18/2415 , G06F18/2431 , G06F18/253 , G06F18/254 , G06N3/04 , G06N3/045 , G06N5/022 , G06N5/025 , G06V10/772 , G06V10/774 , G06V10/806 , G06V10/809 , G06V30/1914 , G06V30/1918 , G06V30/19107 , G06V30/19147
摘要: A method and system for training a neural network are provided. The method includes receiving an input image, selecting at least one data augmentation method from a pool of data augmentation methods, generating an augmented image by applying the selected at least one data augmentation method to the input image, and generating a mixed image from the input image and the augmented image.
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公开(公告)号:US20240153295A1
公开(公告)日:2024-05-09
申请号:US17983557
申请日:2022-11-09
申请人: The Boeing Company
发明人: Ali Azad , David D. Pokrajac , Amir Sadrpour , Barnabas Poczos , Hai Thanh Pham
IPC分类号: G06V30/19 , G06V30/148
CPC分类号: G06V30/1914 , G06V30/158
摘要: Systems and methods for generating optical character recognition (OCR) models configured to identify characters from a variety of different documents. The OCR models are based on a base model. One or more outside models can be tested to determine their effectiveness in supplementing the base model. When an outside model is effective it is incorporated into the base model. Generations of base models can be created that provide for additional functionality that is not present in the preceding model. A family of the generations of base models is maintained.
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公开(公告)号:US20240071037A1
公开(公告)日:2024-02-29
申请号:US18203185
申请日:2023-05-30
发明人: Ming-Jung SEOW , Gang XU , Tao YANG , Wesley Kenneth COBB
IPC分类号: G06V10/32 , G06F18/2137 , G06F18/23 , G06F18/28 , G06N7/01 , G06V10/762 , G06V30/19 , G06V30/262 , H01B1/02
CPC分类号: G06V10/32 , G06F18/2137 , G06F18/23 , G06F18/28 , G06N7/01 , G06V10/762 , G06V30/19127 , G06V30/1914 , G06V30/268 , H01B1/02
摘要: Techniques are disclosed for generating a sequence of symbols based on input data for a neuro-linguistic model. The model may be used by a behavior recognition system to analyze the input data. A mapper component of a neuro-linguistic module in the behavior recognition system receives one or more normalized vectors generated from the input data. The mapper component generates one or more clusters based on a statistical distribution of the normalized vectors. The mapper component evaluates statistics and identifies statistically relevant clusters. The mapper component assigns a distinct symbol to each of the identified clusters.
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公开(公告)号:US11710332B2
公开(公告)日:2023-07-25
申请号:US17543315
申请日:2021-12-06
申请人: Bill.com, LLC
发明人: Changlin Zhang , Bryan Wang , Akshay Ukey , Sangam Singh
IPC分类号: G06V30/414 , G06V30/418 , G06V30/416 , G06F18/28 , G06V30/19 , G06V10/74 , G06V30/10
CPC分类号: G06V30/414 , G06F18/28 , G06V10/761 , G06V30/1914 , G06V30/416 , G06V30/418 , G06V30/10
摘要: Methods, systems, and computer storage media are provided for data extraction. A target document representation may be generated based on modified text of a target electronic document. A measure of similarity may be determined between the target document representation and a reference document representation, which may be based on modified text of a reference electronic document. Based on the measure of similarity, the reference document representation may be selected. An extraction model associated with the selected reference document representation can then be used to extract data from the target document.
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