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公开(公告)号：US11954929B2
公开(公告)日：2024-04-09
申请号：US18186001
申请日：2023-03-17
申请人： DIMAAG-AI, Inc.
IPC分类号： G06V30/19 , G06F16/35 , G06V30/182
CPC分类号： G06V30/19107 , G06F16/35 , G06V30/1823
摘要： The failure modes of mechanical components may be determined based on text analysis. For example, a word embedding may be determined based on a plurality of text documents that include a plurality of maintenance records characterizing failure of mechanical components. A vector representation for a particular maintenance record may then be determined based on the word embedding. Based on the vector representation, the particular maintenance record may then be identified as belonging to a particular failure mode out of a set of possible failure modes.
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公开(公告)号：US11847159B2
公开(公告)日：2023-12-19
申请号：US17812341
申请日：2022-07-13
申请人： Adobe Inc.
IPC分类号： G06F16/56 , G06V30/182 , G06F16/901 , G06V30/262 , G06V30/10 , G06V30/19
CPC分类号： G06F16/56 , G06F16/9014 , G06V30/1823 , G06V30/262 , G06V30/10 , G06V30/19027
摘要： Systems, methods, and non-transitory computer-readable media are disclosed for determining a glyph and a font from a vector outline by applying various combinations of hash-based querying, path-descriptor matching, or anchor-point matching. For example, the disclosed systems can select a subset of candidate glyphs for a vector outline based on (i) comparing hash keys of candidate glyphs with a point-order-agnostic hash key corresponding to the vector outline and (ii) comparing a path descriptor for a primary path of the vector outline to path descriptors corresponding to candidate glyphs. By further comparing anchor points between the vector outline and the subset of candidate glyphs, the disclosed systems can select both a glyph and a font matching the vector outline.
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公开(公告)号：US11922710B2
公开(公告)日：2024-03-05
申请号：US17659656
申请日：2022-04-18
发明人： Chien-Hao Chen , Chao-Hsun Yang , Shih-Tse Chen
IPC分类号： G06V30/19 , G06V30/18 , G06V30/182
CPC分类号： G06V30/19073 , G06V30/18019 , G06V30/1823
摘要： A character recognition method includes the following operations: determining that the image of character to be identified corresponds to a matching character of several registered characters according to several vector distances to be identified between a vector of an image of character to be identified and several vectors of several registered character images of several registered characters, and storing a matching vector distance between the vector of the image of character to be identified and a vector of the matching character by a processor; and storing a data of the matching character according to the image of character to be identified when the matching vector distance is less than a vector distance threshold by the processor.
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公开(公告)号：US20240096121A1
公开(公告)日：2024-03-21
申请号：US17932639
申请日：2022-09-15
发明人： Zhong Fang YUAN , Tong LIU , Yi Chen ZHONG , Xiang Yu YANG , Guan Chao LI
IPC分类号： G06V30/148 , G06V10/774 , G06V10/82 , G06V30/182
CPC分类号： G06V30/153 , G06V10/7747 , G06V10/82 , G06V30/1823
摘要： Provided are a computer program product, system, and method for training and using a vector encoder to determine vectors for sub-images of text in an image to subject to optical character recognition. A vector encoder is trained to encode images representing text into vectors in a vector space. Vectors of images representing similar text have a high degree of cohesion in the vector space. Vectors of images representing dissimilar text have a low degree of cohesion in the vector space. An input image is processed to determine sub-images of the input image that bound text represented in the input image. The sub-images are inputted to the vector encoder to output sub-image vectors. The vector encoder generates a search vector for search text. Optical character recognition is applied to at least one region of the input image including the sub-images having sub-image vectors matching the search vector.
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公开(公告)号：US20240071117A1
公开(公告)日：2024-02-29
申请号：US17894011
申请日：2022-08-23
发明人： Ibrahim GHALYAN , Binlin CHI
IPC分类号： G06V30/32 , G06N3/04 , G06V30/182
CPC分类号： G06V30/32 , G06N3/0472 , G06V30/1823
摘要： The systems and methods relate to electronic signature verification based on topological stochastic models (TSM). The TSM may be trained on samples of known authentic signatures of a signee. Training the TSM may include TSM features extraction on the training samples to extract feature vectors, TSM features aggregation to aggregate the feature vectors, and optimal threshold estimation to determine an optimal threshold value. The optimal threshold value and overall aggregate of feature vectors may be used to evaluate feature vectors extracted from a signature to be verified. For example, a distance between the resulting feature vector extracted from the input sequence and the aggregated feature vector is determined. The distance is compared to the optimal threshold value to determine whether the signature in the input image is verified. The signature in the input image is verified if the distance is less than or equal to the optimal threshold value.
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公开(公告)号：US12079728B2
公开(公告)日：2024-09-03
申请号：US16980380
申请日：2019-03-07
发明人： Chihiro Watanabe , Kaoru Hiramatsu , Kunio Kashino
CPC分类号： G06N3/084 , G06F18/217 , G06F18/23 , G06V30/1823 , G06V30/19173 , G06V30/10
摘要： The present invention enables the structure of a neural network to be quantitatively analyzed. An analyzing unit calculates, for each of combinations of a dimension of input data and a cluster, a sum of squared errors between an output of each unit belonging to the cluster when a value of the dimension of the input data is replaced with an average value of the dimension of the input data included in learning data and an output of each unit belonging to the cluster for the input data before replacement as a relationship between the combinations, and calculates, for each of combinations of the cluster and a dimension of output data, a squared error between the value of the dimension of the output data when an output value of each unit belonging to the cluster is replaced with an average output value of each unit of the cluster when the input data included in the learning data was input and the value of the dimension of the output data before replacement as a relationship between the combinations.
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公开(公告)号：US20240054800A1
公开(公告)日：2024-02-15
申请号：US18186001
申请日：2023-03-17
申请人： DIMAAG-AI, Inc.
IPC分类号： G06V30/19 , G06V30/182 , G06F16/35
CPC分类号： G06V30/19107 , G06V30/1823 , G06F16/35
摘要： The failure modes of mechanical components may be determined based on text analysis. For example, a word embedding may be determined based on a plurality of text documents that include a plurality of maintenance records characterizing failure of mechanical components. A vector representation for a particular maintenance record may then be determined based on the word embedding. Based on the vector representation, the particular maintenance record may then be identified as belonging to a particular failure mode out of a set of possible failure modes.
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