公开(公告)号：US20200159828A1

公开(公告)日：2020-05-21

申请号：US16196153

申请日：2018-11-20

Applicant: SAP SE

Inventor： Christian REISSWIG ,  Eduardo VELLASQUES ,  Sohyeong KIM ,  Darko VELKOSKI ,  Hung Tu DINH

IPC: G06F17/27 ,  G06N3/08 ,  G06N3/04

Abstract: Disclosed herein are system, method, and computer program product embodiments for robust key value extraction. In an embodiment, one or more hierarchical concepts units (HCUs) may be configured to extract key value and hierarchical information from text inputs. The HCUs may use a convolutional neural network, a recurrent neural network, and feature selectors to analyze the text inputs using machine learning techniques to extract the key value and hierarchical information. Multiple HCUs may be used together and configured to identify different categories of hierarchical information. While multiple HCUs may be used, each may use a skip connection to transmit extracted information to a feature concatenation layer. This allows an HCU to directly send a concept that has been identified as important to the feature concatenation layer and bypass other HCUs.

公开(公告)号：US20230075369A1

公开(公告)日：2023-03-09

申请号：US17469075

申请日：2021-09-08

Applicant: SAP SE

Inventor： Sohyeong KIM ,  Christian REISSWIG

IPC: G06N20/20 ,  G06K9/62

Abstract: Systems and methods include training of each of a plurality of models based on a first set of training data comprising a first plurality of pairs, each of the first plurality of pairs comprising a feature and a corresponding label, inputting of each of a plurality of features into each of the plurality of trained models to generate, for each feature of the plurality of features, a plurality of output labels, determining, for each of the plurality of features, a pseudo-label based on the plurality of output labels generated for the feature, determining a second set of training data comprising a second plurality of pairs, each of the second plurality of pairs comprising one of the plurality of features and a pseudo-label determined for the one of the plurality of features, and training an inference model to output an inferred label based on the first set of training data and the second set of training data.

公开(公告)号：US20210150202A1

公开(公告)日：2021-05-20

申请号：US16689516

申请日：2019-11-20

Applicant: SAP SE

Inventor： Timo DENK ,  Christian REISSWIG

IPC: G06K9/00 ,  G06T7/70 ,  G06K9/72

Abstract: Disclosed herein are system, method, and computer program product embodiments for analyzing contextual symbol information for document processing. In an embodiment, a language model system may generate a vector grid that incorporates contextual document information. The language model system may receive a document file and identify symbols of the document file to generate a symbol grid. The language model system may also identify position parameters corresponding to each of the symbols. The language model system may then analyze the symbols using an embedding function and neighboring symbols to determine contextual vector values corresponding to each of the symbols. The language model system may then generate a vector grid mapping the contextual vector values using the position parameters. The contextual information from the vector grid may provide increase document processing accuracy as well as faster processing convergence.

公开(公告)号：US20200302208A1

公开(公告)日：2020-09-24

申请号：US16359012

申请日：2019-03-20

Applicant: SAP SE

Inventor： Johannes HOEHNE ,  Christian REISSWIG ,  Anoop Raveendra KATTI ,  Marco SPINACI

IPC: G06K9/34 ,  G06N3/08 ,  G06K9/00 ,  G06N20/00

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, assign index values to the characters, and recognize different formatting of the characters, such as distinguishing between handwritten and typewritten 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 segmentation masks, such as a type grid 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：US20220171967A1

公开(公告)日：2022-06-02

申请号：US17107223

申请日：2020-11-30

Applicant: SAP SE

Inventor： Christian REISSWIG

IPC: G06K9/00 ,  G06F40/30

Abstract: Disclosed herein are system, method, and computer program product embodiments for correcting extracted document information based on generated confidence and correctness scores. In an embodiment, a document correcting system may receive a document and document information that represents information extracted from the document. The document correcting system may determine the correctness of the document information by processing the document to generate a character grid representing textual information and spatial arrangements for the text within the document. The document correcting system may apply a convolutional neural network on character grid and the document information. The convolutional neural network may output corrected document information, a correctness value indicating the possible errors in the document information, and a confidence value indicating a likelihood of the possible errors.

公开(公告)号：US20220092328A1

公开(公告)日：2022-03-24

申请号：US17029180

申请日：2020-09-23

Applicant: SAP SE

Inventor： Johannes HOEHNE ,  Christian REISSWIG

IPC: G06K9/34 ,  G06K9/46 ,  G06T7/11 ,  G06F16/903

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.