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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
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.
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公开(公告)号:US20240177011A1
公开(公告)日:2024-05-30
申请号:US18071231
申请日:2022-11-29
Applicant: SAP SE
Inventor: MAREK POLEWCZYK , Marco SPINACI , Xiang YU
IPC: G06N3/09
CPC classification number: G06N3/09
Abstract: Various embodiments for a neural network clustering system are described herein. An embodiment operates by detecting a plurality of bounding boxes and identifying coordinates for each of the bounding boxes. An adjacency matrix is generated based on combining a key matrix and a query matrix. The plurality of words are clustered into a plurality of clusters, each cluster corresponding to a different line on the first document. A second document is generated in which the plurality of words corresponding to a respective cluster of the plurality of clusters is arranged on a same line on the second document. The second document is provided for display.
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