Invention Grant
- Patent Title: Model-independent confidence values for extracted document information using a convolutional neural network
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Application No.: US17107223Application Date: 2020-11-30
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Publication No.: US11557140B2Publication Date: 2023-01-17
- Inventor: Christian Reisswig
- Applicant: SAP SE
- Applicant Address: DE Walldorf
- Assignee: SAP SE
- Current Assignee: SAP SE
- Current Assignee Address: DE Walldorf
- Agency: Sterne, Kessler, Goldstein & Fox P.L.L.C.
- Main IPC: G06V30/416
- IPC: G06V30/416 ; 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.
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