Implementing and Evaluating a Font Recommendation System Through Emotion-Based Content-Font Mapping
Source abstract
Abstract
Rapid digital content growth demands pivotal font selection for design and communication. Our study focuses on a font recommendation system that aligns fonts with content emotions. To achieve this, we define font-emotions and quantify them. Additionally, we leverage deep learning techniques for content analysis. Understanding common emotional perceptions, we aimed to align fonts with content emotions. After evaluating diverse mapping methods, we determined a correlation analysis-based model to be most effective. Implementing this model, we verified its utility through usability evaluations. Our proposed system not only assists users with limited design knowledge in receiving contextually fitting font suggestions but also extends its application across various digital content realms.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Web Data Mining and Analysis · Innovative Human-Technology Interaction · Data Visualization and Analytics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of IT Engineering, Research Institute of ICT Convergence, Sookmyung Women’s University, Seoul 04310, Republic of Korea
Visual Communication Design, School of Design, Hongik University, Seoul 04066, Republic of Korea
Department of Visual Arts, Mokpo National University, Muan-gun 58554, Republic of Korea
School of Computer Science, Semyung University, Jecheon 27136, Republic of Korea
Funding
National Research Foundation of Korea (NRF)
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