Learning Typographic Style
원문 초록
Typography is a ubiquitous art form that affects our understanding, perception, and trust in what we read. Thousands of different font-faces have been created with enormous variations in the characters. In this paper, we learn the style of a font by analyzing a small subset of only four letters. From these four letters, we learn two tasks. The first is a discrimination task: given the four letters and a new candidate letter, does the new letter belong to the same font? Second, given the four basis letters, can we generate all of the other letters with the same characteristics as those in the basis set? We use deep neural networks to address both tasks, quantitatively and qualitatively measure the results in a variety of novel manners, and present a thorough investigation of the weaknesses and strengths of the approach.
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FontDiffuser: One-Shot Font Generation via Denoising Diffusion with Multi-Scale Content Aggregation and Style Contrastive Learning
DeepCalliFont: Few-shot Chinese Calligraphy Font Synthesis by Integrating Dual-modality Generative Models
Persis: A Persian Font Recognition Pipeline Using Convolutional Neural Networks
토론
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