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Automated Typographic Font Generation Using Artificial Intelligence

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Abstract

This article investigates the intersection of artificial intelligence (AI) and typography, examining the progress made in font creation using generative adversarial neural networks (GANs) and stylistic descriptors. Using GANs, the research demonstrates a new method to automate and enhance the typeface creation process by employing competitive learning between a generator and a discriminator network. AI algorithms can revolutionize font customization and localization by effectively generating typefaces that comply with specified design preferences using stylistic descriptors. The study underscores the importance of automated font creation in digital design, emphasizing its ability to enhance brand recognition, facilitate multilingual communication, and promote inclusion in digital typography. The paper showcases the effectiveness of the proposed approach in producing top-notch typefaces by conducting empirical analysis and comparing it with existing models. It also identifies potential areas for future research and enhancement in font generation technologies.

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References

  1. Mao, W., Yang, S., Shi, H., Liu, J., Wang, Z.: Intelligent typography: artistic text style transfer for complex texture and structure. IEEE Trans. Multimedia25, 6485–6498 (2023).https://doi.org/10.1109/TMM.2022.3209870

    Article MATH  Google Scholar 

  2. Shi, S., You, W., Han, K., Song, J., Sun, L.: Variable typography: artificial intelligence augmented reading experience. in international design engineering technical conferences and computers and information in engineering conference. In: American Society of Mechanical Engineers, vol. 87295, p. V002T02A088 (2023)

    Google Scholar 

  3. Pan, Z., Yu, W., Yi, X., Khan, A., Yuan, F., Zheng, Y.: Recent progress on generative adversarial networks (GANs): a survey. IEEE Access7, 36322–36333 (2019)

    Article  Google Scholar 

  4. Saxena, D., Cao, J.: Generative adversarial networks (GANs) challenges, solutions, and future directions. ACM Comput. Surv. (CSUR)54(3), 1–42 (2021)

    Article MATH  Google Scholar 

  5. Pham, T.T.T., Pham, T.D., Ta, V.C.: Evaluation of GAN-based models for phishing URL classifiers. Int. J. Comput. Netw. Inf. Secur.15(2), 1–14 (2023).https://doi.org/10.5815/ijcnis.2023.02.01

  6. Sarangi, B., Tripathy, B.: Outlier detection technique for wireless sensor network using GAN with autoencoder to increase the network lifetime. Int. J. Comput. Netw. Inf. Secur.15(1), 26–38 (2023).https://doi.org/10.5815/ijcnis.2023.01.03

    Article MATH  Google Scholar 

  7. Fedushko, S., Dmytriv, A., Topylko, S., Buchii, N.: Efficiency evaluation of the design of polygraphy products in the company online marketing. In: CEUR Workshop Proceedings, vol. 2654, pp. 144–154 (2020).https://ceur-ws.org/Vol-2654/paper11.pdf

  8. Cai, T., et al.: Personalized font recommendations: combining ml and typographic guidelines to optimize readability. In: Designing Interactive Systems Conference, pp. 1–25 (2022)

    Google Scholar 

  9. Yadav, N., Sinha, A., Jain, M., Agrawal, A., Francis, S.: Generation of images from text using AI. Int. J. Eng. Manuf.14(1), 24–37 (2024).https://doi.org/10.5815/ijem.2024.01.03

    Article MATH  Google Scholar 

  10. Ferdousi, B., Mc, G.T.: A survey of artificial life and nature-inspired techniques in computer graphics and visualization. Int. J. Image Graph. Sign. Process.16(1), 1–13 (2024).https://doi.org/10.5815/ijigsp.2024.01.01

    Article MATH  Google Scholar 

  11. Tashev, A., Takenova, Z., Arshidinova, M.: Algorithms for solving problems of resources allocation in the management of business processes in educational organizations. Int. J. Mod. Educ. Comput. Sci.15(5), 14–27 (2023).https://doi.org/10.5815/ijmecs.2023.05.02

    Article MATH  Google Scholar 

  12. Goodfellow, I., et al.: Generative adversarial networks. Commun. ACM63(1), 139–144 (2014).https://doi.org/10.1145/3422622

    Article MATH  Google Scholar 

  13. Park, J., Hassan, A., Choi, J.: CCFont: a component-based Chinese font generation model using generative adversarial networks (GANs). Appl. Sci.12(16), 8005 (2022)

    Article MATH  Google Scholar 

  14. Jiang, Y., Lian, Z., Tang, Y., Xiao, J.: DCFont: an end-to-end deep Chinese font generation system. In: SA 2017 Technical Briefs, Association for Computing Machinery, USA. Technical Briefs, Thailand, 27–30 November (2017).https://doi.org/10.1145/3145749.3149440

  15. Isola, P., Zhu, J.-Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, pp. 5967–5976 (2017).https://doi.org/10.1109/CVPR.2017.632

  16. Liu, M.-Y., et al.: Few-shot unsupervised image-to-image translation. In: Proceedings of the IEEE International Conference on Computer Vision (2019).http://iccv2019.thecvf.com/

  17. Park, H., Yoo, Y., Kwak, N.: MC-GAN: multi-conditional generative adversarial network for image synthesis. arXiv (2018)

    Google Scholar 

  18. Zhang, Y., Zhang, Y., Cai, W.: Separating style and content for generalized style transfer. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, pp. 8447–8455 (2018).https://doi.org/10.1109/CVPR.2018.00881

  19. Gao, Y., Guo, Y., Lian, Z., Tang, Y., Xiao, J.: Artistic glyph image synthesis via one-stage few-shot learning. ACM Trans. Graph.38(6), 1–12 (2019)

    Google Scholar 

  20. Li, C., Taniguchi, Y., Lu, M., Konomi, S., Nagahara, H.: Cross-language font style transfer. Appl. Intell. (53), 1–15 (2023)

    Google Scholar 

  21. PANOSE font classification system Metrics Guide: pan1

    Google Scholar 

  22. He, H., Jin, X., Chen, A.: GAS-NeXt: a few-shot cross-lingual font generator. arXiv (2022).https://doi.org/10.48550/arXiv.2212.02886

  23. Google Fonts Files: Google (2023).https://fonts.google.com/

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Acknowledgments

This study was part of research funded by the National Research Foundation of Ukraine funded this research under project number 2021.01/0103, British Academy fellowship number RaR\100727, and H2020 project ZEBAI, # 101138678, NextGenerationEU through the Recovery and Resilience Plan for Slovakia under the project No. 09I03-03-V01-000153.

Author information

Authors and Affiliations

  1. Lviv Polytechnic National University, Lviv, 79013, Ukraine

    Nataliya Shakhovska, Oleksandr Petrovskyi & Solomiia Fedusko

  2. Brunel University, London, UB8 3PH, UK

    Nataliya Shakhovska

  3. Comenius University Bratislava, Odbojárov 10, 820 05, Bratislava, Slovakia

    Solomiia Fedusko & Michal Greguš Jr.

Authors
  1. Nataliya Shakhovska
  2. Oleksandr Petrovskyi
  3. Solomiia Fedusko
  4. Michal Greguš Jr.

Corresponding author

Correspondence toSolomiia Fedusko.

Editor information

Editors and Affiliations

  1. Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Krakow, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Shakhovska, N., Petrovskyi, O., Fedusko, S., Greguš, M. (2025). Automated Typographic Font Generation Using Artificial Intelligence. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2024. Lecture Notes in Computer Science(), vol 15165. Springer, Cham. https://doi.org/10.1007/978-3-031-84356-3_14

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