Name
Abstract | ||
|---|---|---|
Recently, methods based on deep learning have dominated the field of text recognition. With a large number of training data, most of them can achieve the state-of-the-art performances. However, it is hard to harvest and label sufficient text sequence images from the real scenes. To mitigate this issue, several methods to synthesize text sequence images were proposed, yet they usually need complicated preceding or follow-up steps. In this work, we present a method which is able to generate infinite training data without any auxiliary pre/post-process. We tackle the generation task as an image-to-image translation one and utilize conditional adversarial networks to produce realistic text sequence images in the light of the semantic ones. Some evaluation metrics are involved to assess our method and the results demonstrate that the caliber of the data is satisfactory. The code and dataset will be publicly available soon. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s11063-019-10166-x | Neural Processing Letters |
Keywords | DocType | Volume |
Image generation, Text sequence images, Training data, Text recognition | Journal | 51 |
Issue | ISSN | Citations |
2 | 1370-4621 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanxiang Gong | 1 | 4 | 1.79 |
| Linjie Deng | 2 | 4 | 1.79 |
| Zheng Ma | 3 | 11 | 2.18 |
| Mei Xie | 4 | 1 | 1.36 |