FontMatcher: Font Image Paring for Harmonious Digital Graphic Design

ABSTRACT

One of the important aspects in graphic design is choosing the font of the caption that matches aesthetically the associated image. To obtain a good match, users would exhaustively examine a long font list requiring them a substantial effort. This paper presents FontMatcher, which supports users to design digital graphic works harmoniously pairing fonts with an image. The system provides three features, recommendation, explaination and feedback. If a warm feeling image is given as input, the system recommends warm feeling fonts, and then explains what is the distinguishing features of the recommendation, e.g. a cursive shape. Users can also provide feedback to find fonts which correspond to their intention. Our evaluation results show that the recommended fonts scored better than selected fonts by novices and provides competing results with the ones chosen by experienced graphic designers. The system also provides explanations that help increasing the reliability of the recommended results.

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Index Terms

• Computing methodologies

  • Machine learning

    • Learning paradigms

      • Unsupervised learning

        • Dimensionality reduction and manifold learning

• Information systems

  • Information retrieval

    • Retrieval tasks and goals

      • Recommender systems

    • Specialized information retrieval

      • Multimedia and multimodal retrieval

    • Users and interactive retrieval

      • Search interfaces