Abstract
The selection of text color is a time-consuming and important aspect in the designing of visual-textual presentation layout. In this paper, we propose a novel deep neural network architecture for predicting text color in the designing of visual-textual presentation layout. The proposed architecture consists of a text colorization network, a color harmony scoring network, and a text readability scoring network. The color harmony scoring network is learned by training with color theme data with aesthetic scores. The text readability scoring network is learned by training with design works. Finally, the text colorization network is designed to predict text colors by maximizing both color harmony and text readability, as well as learning from designer’s choice of color. In addition, this paper conducts a comparison with other methods based on random generation, color theory rules or similar features search. Both quantitative and qualitative evaluation results demonstrate that the proposed method has better performance.
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Yang, X., Xu, X., Huang, Y., Yu, N. (2021). HTCN: Harmonious Text Colorization Network for Visual-Textual Presentation Design. In: Ma, H., et al. Pattern Recognition and Computer Vision. PRCV 2021. Lecture Notes in Computer Science(), vol 13020. Springer, Cham. https://doi.org/10.1007/978-3-030-88007-1_46
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