StyleGAN2-ADA 和 Real-ESRGAN:利用生成式对抗网络生成泰文字体

Nidchapan Nitisukanan, Chotika Boonthaweechok, Prapatsorn Tiawpanichkij, Juthamas Pissakul, Naliya Maneesawangwong, Thitirat Siriborvornratanakul
{"title":"StyleGAN2-ADA 和 Real-ESRGAN:利用生成式对抗网络生成泰文字体","authors":"Nidchapan Nitisukanan,&nbsp;Chotika Boonthaweechok,&nbsp;Prapatsorn Tiawpanichkij,&nbsp;Juthamas Pissakul,&nbsp;Naliya Maneesawangwong,&nbsp;Thitirat Siriborvornratanakul","doi":"10.1007/s43674-024-00069-3","DOIUrl":null,"url":null,"abstract":"<div><p>Contemporary font design is a labor-intensive process. To address this, we utilize deep learning, specifically StyleGAN2-ADA and Real-ESRGAN, for automated Thai font generation. StyleGAN2-ADA incorporates adaptive discriminator augmentation (ADA) for image synthesis. By integrating Real-ESRGAN, font quality is enhanced. Our approach produces diverse, high-resolution fonts, as demonstrated in comparative experiments. In a survey with 50 participants, StyleGAN2-ADA without augmentation proves superior in legibility and visual appeal, while StyleGAN2-ADA with augmentation excels in diversity. This research highlights the efficiency of deep learning in creating high-quality Thai fonts and has implications for automated font design advancement.</p></div>","PeriodicalId":72089,"journal":{"name":"Advances in computational intelligence","volume":"4 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"StyleGAN2-ADA and Real-ESRGAN: Thai font generation with generative adversarial networks\",\"authors\":\"Nidchapan Nitisukanan,&nbsp;Chotika Boonthaweechok,&nbsp;Prapatsorn Tiawpanichkij,&nbsp;Juthamas Pissakul,&nbsp;Naliya Maneesawangwong,&nbsp;Thitirat Siriborvornratanakul\",\"doi\":\"10.1007/s43674-024-00069-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Contemporary font design is a labor-intensive process. To address this, we utilize deep learning, specifically StyleGAN2-ADA and Real-ESRGAN, for automated Thai font generation. StyleGAN2-ADA incorporates adaptive discriminator augmentation (ADA) for image synthesis. By integrating Real-ESRGAN, font quality is enhanced. Our approach produces diverse, high-resolution fonts, as demonstrated in comparative experiments. In a survey with 50 participants, StyleGAN2-ADA without augmentation proves superior in legibility and visual appeal, while StyleGAN2-ADA with augmentation excels in diversity. This research highlights the efficiency of deep learning in creating high-quality Thai fonts and has implications for automated font design advancement.</p></div>\",\"PeriodicalId\":72089,\"journal\":{\"name\":\"Advances in computational intelligence\",\"volume\":\"4 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-02-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in computational intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s43674-024-00069-3\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in computational intelligence","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.1007/s43674-024-00069-3","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

现代字体设计是一个劳动密集型过程。为了解决这个问题,我们利用深度学习,特别是 StyleGAN2-ADA 和 Real-ESRGAN,来自动生成泰文字体。StyleGAN2-ADA 将自适应判别器增强(ADA)用于图像合成。通过集成 Real-ESRGAN,字体质量得到了提升。对比实验证明,我们的方法可以生成多样化、高分辨率的字体。在一项有 50 名参与者参与的调查中,没有增强功能的 StyleGAN2-ADA 在可读性和视觉吸引力方面更胜一筹,而有增强功能的 StyleGAN2-ADA 则在多样性方面表现出色。这项研究凸显了深度学习在创建高质量泰文字体方面的效率,并对自动字体设计的发展具有重要意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
StyleGAN2-ADA and Real-ESRGAN: Thai font generation with generative adversarial networks

Contemporary font design is a labor-intensive process. To address this, we utilize deep learning, specifically StyleGAN2-ADA and Real-ESRGAN, for automated Thai font generation. StyleGAN2-ADA incorporates adaptive discriminator augmentation (ADA) for image synthesis. By integrating Real-ESRGAN, font quality is enhanced. Our approach produces diverse, high-resolution fonts, as demonstrated in comparative experiments. In a survey with 50 participants, StyleGAN2-ADA without augmentation proves superior in legibility and visual appeal, while StyleGAN2-ADA with augmentation excels in diversity. This research highlights the efficiency of deep learning in creating high-quality Thai fonts and has implications for automated font design advancement.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Non-linear machine learning with sample perturbation augments leukemia relapse prognostics from single-cell proteomics measurements ARBP: antibiotic-resistant bacteria propagation bio-inspired algorithm and its performance on benchmark functions Detection and classification of diabetic retinopathy based on ensemble learning Office real estate price index forecasts through Gaussian process regressions for ten major Chinese cities Systematic micro-breaks affect concentration during cognitive comparison tasks: quantitative and qualitative measurements
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1