{"title":"StyleGAN2-ADA 和 Real-ESRGAN:利用生成式对抗网络生成泰文字体","authors":"Nidchapan Nitisukanan, Chotika Boonthaweechok, Prapatsorn Tiawpanichkij, Juthamas Pissakul, Naliya Maneesawangwong, 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, Chotika Boonthaweechok, Prapatsorn Tiawpanichkij, Juthamas Pissakul, Naliya Maneesawangwong, 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}
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.