Name
Abstract | ||
|---|---|---|
The purpose of this paper is to reveal the ability that Convolutional Neural Networks (CNN) have on the novel task of image-to-image language conversion. We propose a new network to tackle this task by converting images of Korean Hangul characters directly into images of the phonetic Latin character equivalent. The conversion rules between Hangul and the phonetic symbols are not explicitly provided. The results of the proposed network show that it is possible to perform image-to-image language conversion. Moreover, it shows that it can grasp the structural features of Hangul even from limited learning data. In addition, it introduces a new network to use when the input and output have significantly different features. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICDAR.2019.00078 | 2019 International Conference on Document Analysis and Recognition (ICDAR) |
Keywords | Field | DocType |
convolutional neural network,U-Net,image-to-image conversion,translation | GRASP,Pattern recognition,Computer science,Convolutional neural network,Input/output,Artificial intelligence,Hangul | Conference |
ISSN | ISBN | Citations |
1520-5363 | 978-1-7281-3015-6 | 0 |
PageRank | References | Authors |
0.34 | 10 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kohei Baba | 1 | 0 | 0.34 |
| Seiichi Uchida | 2 | 790 | 105.59 |
| Brian Kenji Iwana | 3 | 7 | 6.58 |