给九种印度文字造字体，不再从零画，而是给现成字体换皮
造一款印度文字的字体，难点不在画字母，而在连字：几百上千种组合形式必须互相咬合，稍有偏差整款字体就废。这篇论文换了个思路：不从头生成，而是拿一款已经调好的字体当模板，只重画字形轮廓，连字表和排版数据原样保留，输出就是完整可安装的字体。他们用这个方法产出了66款字体，覆盖九种印度文字。但论文自己承认：在56个测试字体里，直接复制模板反而比AI生成的效果更好，风格迁移的效果只有用他们自己训练的模型才能测出来。所以这不是一个能让你明天就用上的工具，而是一个值得留意的方向——当AI生成复杂文字系统的字体时，也许该先学会站在已有作品肩上。
📄 原文摘要(英文)
We present Srijika, a system for producing installable OpenType fonts for nine Brahmic scripts: Devanagari, Tamil, Bengali, Telugu, Kannada, Malayalam, Gujarati, Gurmukhi, and Odia. Rather than generating fonts from scratch, Srijika restyles glyph outlines from shaping-complete template fonts. It preserves the template's cmap and GSUB closure and its GPOS data under a documented metric policy, making every output a complete font by construction. This addresses a central challenge of Indic font generation: hundreds to thousands of conjuncts, half forms, and matra variants must remain mutually consistent under OpenType shaping. Srijika produces 66 TTFs: 57 curated presets and nine open-vocabulary showcase fonts. All pass the OpenType Sanitizer, while HarfBuzz and CoreText reproduce the template glyph-ID sequences on conjunct-heavy probes. A full-closure audit covering 80,915 glyphs and 54,812 anchors quantifies metric changes. Natural-language style selection uses Lipika, a retrieval index over approximately 650 open-license font families. A reference-conditioned latent diffusion model redraws template glyphs in the selected style, followed by content gating, harmonization, and shaped-cluster verification with fallback to template outlines. We evaluate against no-learning baselines. On diffusion-training-family-held-out SSIM gates, template copying outperforms generation on 50 of 56 faces. Style movement is measurable only with an internal same-model embedding whose training corpus includes the held-out families, so these results require caution. A learned baseline, independent style metric, and human study are outside this report's scope. Our contributions are the layout-reusing formulation and pipeline, its nine-script audit and benchmark, and a negative-results catalogue covering failed conditioning, objective choices, and data-hull limits of reference-guided restyling.