Font recognition and font similarity learning using a deep neural network
US9501724B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 9, 2015 |
| Grant date | Nov 22, 2016 |
| Priority date | — |
| Expiry date | Jun 9, 2035 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/048
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A convolutional neural network (CNN) is trained for font recognition and font similarity learning. In a training phase, text images with font labels are synthesized by introducing variances to minimize the gap between the training images and real-world text images. Training images are generated and input into the CNN. The output is fed into an N-way softmax function dependent on the number of fonts the CNN is being trained on, producing a distribution of classified text images over N class labels. In a testing phase, each test image is normalized in height and squeezed in aspect ratio resulting in a plurality of test patches. The CNN averages the probabilities of each test patch belonging to a set of fonts to obtain a classification. Feature representations may be extracted and utilized to define font similarity between fonts, which may be utilized in font suggestion, font browsing, or font recognition applications.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.