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公开(公告)号：US11341760B2
公开(公告)日：2022-05-24
申请号：US17007704
申请日：2020-08-31
Applicant: SAP SE
Inventor： Johannes Hoehne , Konrad Schenk
IPC: G06V30/413 , G06F40/166 , G06F40/205
Abstract: Disclosed herein are various embodiments for an augmented reality interaction, modeling, and annotation system. An embodiment operates by receiving an image including unknown data in an unknown format, including pixels. Each of the pixels is classified as one of a background pixel, a key pixel, or a value pixel representing the unknown data. For a plurality of the pixels classified as key pixels or value pixels, a plurality of locational data values associated with the unknown format are generated. Based on the locational data values, a key image and a corresponding value image from the received image are identified. The key image and the corresponding value image are output.
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公开(公告)号：US10915786B2
公开(公告)日：2021-02-09
申请号：US16288357
申请日：2019-02-28
Applicant: SAP SE
Inventor： Johannes Hoehne , Anoop Raveendra Katti , Christian Reisswig , Marco Spinaci
IPC: G06K9/62
Abstract: Disclosed herein are system, method, and computer program product embodiments for providing object detection and filtering operations. An embodiment operates by receiving an image comprising a plurality of pixels and pixel information for each pixel. The pixel information indicates a bounding box corresponding to an object within the image associated with a respective pixel and a confidence score associated with the bounding box for the respective pixel. Pixels that do not correspond to a center of at least one of the bounding boxes are iteratively removed from the plurality of pixels until a subset of pixels each of which correspond to a center of at least one of the bounding boxes remains. Based on the subset, a final bounding box associated with each object of the image is determined based on an overlapping of the bounding boxes of the subset of pixels and the corresponding confidence scores.
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公开(公告)号：US11663635B2
公开(公告)日：2023-05-30
申请号：US16413058
申请日：2019-05-15
Applicant: SAP SE
Inventor： Julian Stoettinger , Volker Loch , Rolf Mahr , Rohit Kumar Gupta , Johannes Hoehne
IPC: G06N5/046 , G06N20/00 , G06F18/2431 , G06Q30/0282 , G06F16/28
CPC classification number: G06Q30/0282 , G06F16/285 , G06F18/2431 , G06N5/046 , G06N20/00
Abstract: Provided is a system and method that can identify whether an item is a dangerous good. The system can determine whether a product belongs in any of a number of different classes of dangerous goods from among a plurality of different regulations based on a machine learning algorithm which performs a text-based classification. In one example, the method may include receiving an identification of an object, retrieving a plurality of descriptive attributes of the object from a data store and converting the plurality of descriptive attributes into an input string, predicting whether the object is a dangerous object via execution of a text-based machine learning algorithm that receives the input string as an input, and outputting information about the prediction of the object for display via a user interface.
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公开(公告)号：US11275969B2
公开(公告)日：2022-03-15
申请号：US16704940
申请日：2019-12-05
Applicant: SAP SE
Inventor： Johannes Hoehne , Marco Spinaci
Abstract: In some embodiments, a method inputs a set of images into a network and trains the network based on a classification of the set of images to one or more characters in a set of characters. The method obtains a set of encodings for the one or more characters based on a layer of the network that restricts the output of the layer to a number of values. Then, the method stores the set of encodings for the one or more characters, wherein an encoding in the set of encodings is retrievable when a corresponding character is determined.
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公开(公告)号：US20200082218A1
公开(公告)日：2020-03-12
申请号：US16123177
申请日：2018-09-06
Applicant: SAP SE
Inventor： Johannes Hoehne , Anoop Raveendra Katti , Christian Reisswig
Abstract: Disclosed herein are system, method, and computer program product embodiments for optical character recognition using end-to-end deep learning. In an embodiment, an optical character recognition system may train a neural network to identify characters of pixel images and to assign index values to the characters. The neural network may also be trained to identify groups of characters and to generate bounding boxes to group these characters. The optical character recognition system may then analyze documents to identify character information based on the pixel data and produce a segmentation mask and one or more bounding box masks. The optical character recognition system may supply these masks as an output or may combine the masks to generate a version of the received document having optically recognized characters.
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Patent Agency Ranking
公开(公告)号：US11281928B1
公开(公告)日：2022-03-22
申请号：US17029180
申请日：2020-09-23
Applicant: SAP SE
Inventor： Johannes Hoehne , Christian Reisswig
IPC: G06K9/34 , G06K9/46 , G06F16/903 , G06T7/11
Abstract: Disclosed herein are system, method, and computer program product embodiments for querying document terms and identifying target data from documents. In an embodiment, a document processing system may receive a document and a query string. The document processing system may perform optical character recognition to obtain character information and positioning information for the characters of the document. The document processing system may generate a two-dimensional character grid for the document. The document processing system may apply a convolutional neural network to the character grid and the query string to identify target data from the document corresponding to the query string. The convolutional neural network may then produce a segmentation mask and/or bounding boxes to identify the targeted data.
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公开(公告)号：US11244208B2
公开(公告)日：2022-02-08
申请号：US16711978
申请日：2019-12-12
Applicant: SAP SE
Inventor： Christian Reisswig , Anoop Raveendra Katti , Steffen Bickel , Johannes Hoehne , Jean Baptiste Faddoul
Abstract: Disclosed herein are system, method, and computer program product embodiments for processing a document. In an embodiment, a document processing system may receive a document. The document processing system may perform optical character recognition to obtain character information and positioning information for the characters. The document processing system may generate a down-sampled two-dimensional character grid for the document. The document processing system may apply a convolutional neural network to the character grid to obtain semantic meaning for the document. The convolutional neural network may produce a segmentation mask and bounding boxes to correspond to the document.
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公开(公告)号：US10915788B2
公开(公告)日：2021-02-09
申请号：US16123177
申请日：2018-09-06
Applicant: SAP SE
Inventor： Johannes Hoehne , Anoop Raveendra Katti , Christian Reisswig
IPC: G06K9/34 , G06K9/62 , G06N3/08 , G06F40/279
Abstract: Disclosed herein are system, method, and computer program product embodiments for optical character recognition using end-to-end deep learning. In an embodiment, an optical character recognition system may train a neural network to identify characters of pixel images and to assign index values to the characters. The neural network may also be trained to identify groups of characters and to generate bounding boxes to group these characters. The optical character recognition system may then analyze documents to identify character information based on the pixel data and produce a segmentation mask and one or more bounding box masks. The optical character recognition system may supply these masks as an output or may combine the masks to generate a version of the received document having optically recognized characters.
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公开(公告)号：US20200279128A1
公开(公告)日：2020-09-03
申请号：US16288357
申请日：2019-02-28
Applicant: SAP SE
Inventor： Johannes Hoehne , Anoop Raveendra Katti , Christian Reisswig , Marco Spinaci
IPC: G06K9/62
Abstract: Disclosed herein are system, method, and computer program product embodiments for providing object detection and filtering operations. An embodiment operates by receiving an image comprising a plurality of pixels and pixel information for each pixel. The pixel information indicates a bounding box corresponding to an object within the image associated with a respective pixel and a confidence score associated with the bounding box for the respective pixel. Pixels that do not correspond to a center of at least one of the bounding boxes are iteratively removed from the plurality of pixels until a subset of pixels each of which correspond to a center of at least one of the bounding boxes remains. Based on the subset, a final bounding box associated with each object of the image is determined based on an overlapping of the bounding boxes of the subset of pixels and the corresponding confidence scores.
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公开(公告)号：US12125075B2
公开(公告)日：2024-10-22
申请号：US18136394
申请日：2023-04-19
Applicant: SAP SE
Inventor： Julian Stoettinger , Volker Loch , Rolf Mahr , Rohit Kumar Gupta , Johannes Hoehne
IPC: G06N5/046 , G06F16/28 , G06F18/2431 , G06N20/00 , G06Q30/0282
CPC classification number: G06Q30/0282 , G06F16/285 , G06F18/2431 , G06N5/046 , G06N20/00
Abstract: Provided is a system and method that can identify whether an item is a dangerous good. The system can determine whether a product belongs in any of a number of different classes of dangerous goods from among a plurality of different regulations based on a machine learning algorithm which performs a text-based classification. In one example, the method may include receiving an identification of an object, retrieving a plurality of descriptive attributes of the object from a data store and converting the plurality of descriptive attributes into an input string, predicting whether the object is a dangerous object via execution of a text-based machine learning algorithm that receives the input string as an input, and outputting information about the prediction of the object for display via a user interface.
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