Name
Abstract | ||
|---|---|---|
Deep neural networks help solving different images related tasks very efficiently, though their cost is high whereas a lot of data are required for training. While there is a great demand to build neural network models for optical character detection and recognition for different languages, such as, for mobile real-time applications, datasets collecting and labeling are quite expensive. In this paper, we propose the fully automated approach for synthetic images with text generation based on deep learning and projective geometry methods. For evaluation, we trained two neural networks on the dataset generated by our algorithm. Our approach enables to decrease the false negative rate on real images from SVT and SVT-50 datasets in comparison with training on SynthText dataset, giving similar to 1% of F-1-measure increasing. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1117/12.2557064 | Proceedings of SPIE |
Keywords | DocType | Volume |
Image generation,neural networks,optical character recognition,semantic segmentation,text detection,text localization | Conference | 11433 |
ISSN | Citations | PageRank |
0277-786X | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Natalia Khanzhina | 1 | 0 | 0.68 |
| Natalia Slepkova | 2 | 0 | 0.34 |
| Andrey Filchenkov | 3 | 46 | 15.80 |