Automatic Generation System of Regional Cultural Symbols Based on Deep Learning
- DOI
- 10.2991/978-2-38476-327-6_31How to use a DOI?
- Keywords
- Deep learning; regional cultural symbols; automatic generation; data analysis; symbol design
- Abstract
This paper presents a comprehensive study on the design and development of an automatic generation system for regional cultural symbols utilizing deep learning techniques. With the rapid advancements in artificial intelligence and particularly in deep learning, there is a growing need to digitize and automate the creation of cultural symbols that reflect the unique identity of various regions. The proposed system aims to streamline the process of symbol generation, ensuring that these symbols not only embody the essence of local cultures but also cater to the demands of modern digital platforms. Through extensive data collection, analysis, and model training, our system demonstrates the potential to generate visually appealing and culturally relevant symbols that can be widely adopted in various applications, including tourism promotion, urban planning, and digital communication.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Junyao Wang AU - Sazrinee Zainal Abidin AU - Khairul Manami Kamarudin AU - Nazlina Shaari PY - 2024 DA - 2024/12/17 TI - Automatic Generation System of Regional Cultural Symbols Based on Deep Learning BT - Proceedings of the 2024 4th International Conference on Social Development and Media Communication (SDMC 2024) PB - Atlantis Press SP - 252 EP - 259 SN - 2352-5398 UR - https://doi.org/10.2991/978-2-38476-327-6_31 DO - 10.2991/978-2-38476-327-6_31 ID - Wang2024 ER -