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.

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

IPC: G06K9/62 ,  G06N3/08 ,  G06F17/27

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.

公开(公告)号：US11301627B2

公开(公告)日：2022-04-12

申请号：US16734880

申请日：2020-01-06

Applicant: SAP SE

Inventor： Rohit Kumar Gupta ,  Johannes Hoehne ,  Anoop Raveendra Katti

IPC: G06F40/274 ,  G06F40/289 ,  G06F40/30 ,  G06V10/20

Abstract: System, method, and various embodiments for providing contextualized character recognition system are described herein. An embodiment operates by determining a plurality of predicted words of an image. An accuracy measure or each of the plurality of predicted words is identified and a replaceable word with an accuracy measure below a threshold is identified. A plurality of candidate words associated with the replaceable word are identified and a probability for each of the candidate words is calculated based on a contextual analysis. One of the candidate words with a highest probability is selected. The plurality of predicted words including the selected candidate word with the highest probability replacing the replaceable word is output.

公开(公告)号：US10783377B2

公开(公告)日：2020-09-22

申请号：US16218067

申请日：2018-12-12

Applicant: SAP SE

Inventor： Anoop Raveendra Katti ,  Shachar Klaiman ,  Marius Lehne ,  Sebastian Brarda ,  Johannes Hoehne ,  Matthias Frank ,  Lennart Van der Goten

IPC: G06K9/00 ,  G06F16/783 ,  G06N3/08

Abstract: Aspects of the present disclosure therefore involve systems and methods for identifying a set of visually similar scenes to a target scene selected or otherwise identified by a match analyst. A scene retrieval platform performs operations for: receiving an input that comprises an identification of a scene; retrieving a set of coordinates based on the scene identified by the input, where the set of coordinates identify positions of the entities depicted within the frames; generating a set of vector values based on the coordinates of the entities depicted within each of the frames; concatenating the set of vector values to generate a concatenated vector value that represents the scene; generating a visual representation of the concatenated vector value; and identifying one or more similar scenes to the scene identified by the input based on the visual representation of the concatenated vector value.

公开(公告)号：US10540579B2

公开(公告)日：2020-01-21

申请号：US15983489

申请日：2018-05-18

Applicant: SAP SE

Inventor： Christian Reisswig ,  Anoop Raveendra Katti ,  Steffen Bickel ,  Johannes Hoehne ,  Jean Baptiste Faddoul

IPC: G06K9/72 ,  G06K9/00 ,  G06N3/04 ,  G06N3/08

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.

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

IPC: G06K9/72 ,  G06K9/00 ,  G06N3/04 ,  G06N3/08

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.

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.

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.

公开(公告)号：US11302108B2

公开(公告)日：2022-04-12

申请号：US16565614

申请日：2019-09-10

Applicant: SAP SE

Inventor： Johannes Hoehne ,  Marco Spinaci ,  Anoop Raveendra Katti

IPC: G06K9/34 ,  G06V30/148 ,  G06N20/00 ,  G06T7/10 ,  G06F16/901 ,  G06F17/18 ,  G06V30/10

Abstract: Disclosed herein are system, method, and computer program product embodiments for optical character recognition (OCR) pre-processing using machine learning. In an embodiment, a neural network may be trained to identify a standardized document rotation and scale expected by an OCR service performing character recognition. The neural network may then analyze a received document image to identify a corresponding rotation and scale of the document image relative to the expected standardized values. In response to this identification, the document image may be modified in the inverse to standardize the rotation and scale of the document image to match the format expected by the OCR service. In some embodiments, a neural network may perform the standardization as well as the character recognition using a shared computation graph.

公开(公告)号：US20210073566A1

公开(公告)日：2021-03-11

申请号：US16565614

申请日：2019-09-10

Applicant: SAP SE

Inventor： Johannes Hoehne ,  Marco Spinaci ,  Anoop Raveendra Katti

IPC: G06K9/34 ,  G06N20/00 ,  G06T7/10 ,  G06F17/18 ,  G06F16/901

Abstract: Disclosed herein are system, method, and computer program product embodiments for optical character recognition (OCR) pre-processing using machine learning. In an embodiment, a neural network may be trained to identify a standardized document rotation and scale expected by an OCR service performing character recognition. The neural network may then analyze a received document image to identify a corresponding rotation and scale of the document image relative to the expected standardized values. In response to this identification, the document image may be modified in the inverse to standardize the rotation and scale of the document image to match the format expected by the OCR service. In some embodiments, a neural network may perform the standardization as well as the character recognition using a shared computation graph.