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TE141K: Artistic Text Benchmark for Text Effect Transfer.

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    This study introduces TE141K, the largest dataset for automatic text effect transfer, featuring 141,081 effect/glyph pairs. A novel Text Effect Transfer GAN (TET-GAN) model demonstrates superior performance in transferring diverse text styles.

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    Area of Science:

    • Computer Vision
    • Computer Graphics
    • Machine Learning

    Background:

    • Text effects enhance visual artistry but are complex to create manually.
    • Existing automatic text effect transfer methods are limited by insufficient data.

    Purpose of the Study:

    • Introduce the TE141K dataset, the largest to date, for text effect transfer.
    • Propose and evaluate a novel Text Effect Transfer GAN (TET-GAN) model.

    Main Methods:

    • Developed the TE141K dataset with 141,081 professionally designed text effect/glyph pairs.
    • Proposed a single Text Effect Transfer GAN (TET-GAN) model capable of transferring 152 styles.
    • Benchmarked TET-GAN against 14 existing style transfer models.

    Main Results:

    • The TE141K dataset is the largest available for text effect transfer.
    • TET-GAN significantly outperforms 14 other models in qualitative and quantitative evaluations.
    • The proposed dataset is effective and presents a challenging benchmark.

    Conclusions:

    • The TE141K dataset effectively addresses the data limitations in text effect transfer.
    • TET-GAN offers a superior and extensible approach for automatic text effect transfer.