Name
Abstract | ||
|---|---|---|
When fonts are used on documents, they are intentionally selected by designers. For example, when designing a book cover, the typography of the text is an important factor in the overall feel of the book. In addition, it needs to be an appropriate font for the rest of the book cover. Thus, we propose a method of generating a book title image based on its context within a book cover. We propose an end-to-end neural network that inputs the book cover, a target location mask, and a desired book title and outputs stylized text suitable for the cover. The proposed network uses a combination of a multi-input encoder-decoder, a text skeleton prediction network, a perception network, and an adversarial discriminator. We demonstrate that the proposed method can effectively produce desirable and appropriate book cover text through quantitative and qualitative results. The code can be found at https://github.com/Taylister/FontFits. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-86334-0_37 | DOCUMENT ANALYSIS AND RECOGNITION, ICDAR 2021, PT III |
Keywords | DocType | Volume |
Text generation, Neural font style transfer, Book covers | Conference | 12823 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Taiga Miyazono | 1 | 0 | 0.68 |
| Brian Kenji Iwana | 2 | 10 | 4.24 |
| Daichi Haraguchi | 3 | 0 | 1.01 |
| Seiichi Uchida | 4 | 790 | 105.59 |