Deep learning based document image embeddings for layout classification and retrieval
US10963692B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 30, 2018 |
| Grant date | Mar 30, 2021 |
| Priority date | — |
| Expiry date | May 9, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/088
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Image documents that have a visually perceptible geometric structure and a plurality of visually perceptible key-value pairs are grouped. The image documents are processed to generate a corresponding textually encoded document. The textually encoded documents are each assigned into one of a plurality of layout groups, wherein all textually encoded documents in a particular layout group share a visually perceptible layout that is substantially similar. Triplets are selected from the layout groups, where two documents are from the same layout group and one document is from a different layout group. The triplets are processed with a convolutional neural network to generate a trained neural network that may be used to classify documents in a production environment such that a template designed on one image document in a group permits an extraction engine to extract all relevant fields on all image documents within the group.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.