Optical character recognition employing deep learning with machine generated training data
US10489682B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 21, 2017 |
| Grant date | Nov 26, 2019 |
| Priority date | — |
| Expiry date | Jun 29, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V30/10
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
An optical character recognition system employs a deep learning system that is trained to process a plurality of images within a particular domain to identify images representing text within each image and to convert the images representing text to textually encoded data. The deep learning system is trained with training data generated from a corpus of real-life text segments that are generated by a plurality of OCR modules. Each of the OCR modules produces a real-life image/text tuple, and at least some of the OCR modules produce a confidence value corresponding to each real-life image/text tuple. Each OCR module is characterized by a conversion accuracy substantially below a desired accuracy for an identified domain. Synthetically generated text segments are produced by programmatically converting text strings to a corresponding image where each text string and corresponding image form a synthetic image/text tuple.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.