arXiv:2609.05661cs.CVcs.GR2026-09

Srijika可批量生成9种印地语系字体,复用已有字形布局实现高效风格重制。

Srijika: OpenType-Layout-Reusing Font Restyling for Nine Indic Scripts

论文配图:Srijika: OpenType-Layout-Reusing Font Restyling for Nine Indic Scripts
图 1 · 摘自论文原文
  • 复用完整字形模板,保持原有排版规则与字距数据不变
  • 生成66个可安装字体,覆盖8万余字形且通过格式校验
  • 适合需要快速定制多语言字体的设计师和开源项目

我们提出Srijika系统,用于为九种婆罗米系文字(天城文、泰米尔文、孟加拉文、泰卢固文、卡纳达文、马拉雅拉姆文、古吉拉特文、旁遮普文、奥里亚文)生成可安装的OpenType字体。该系统不从头生成字形,而是复用已具备完整排版功能的模板字体的字形轮廓。它保留模板的cmap和GSUB闭包以及符合文档规范的GPOS数据,确保输出字体在构建时即为完整字体。这一方法解决了印地语系字体生成的核心难题:数百至数千个复合字符、半形及变音符号必须在OpenType排版下保持一致。Srijika生成了66个TTF字体,包括57个预设样式和9个开放词汇演示字体。所有字体均通过OpenType Sanitizer验证,HarfBuzz和CoreText在高复合字符测试中准确复现模板的字形序列。全闭包审计覆盖80,915个字形与54,812个锚点,量化了度量变化。自然语言风格选择基于Lipika检索索引,涵盖约650个开源许可字体家族。参考条件下的潜在扩散模型将模板字形重绘为指定风格,再经内容过滤、风格调和与形态簇验证,必要时回退至原始轮廓。评估对比无学习基线:在56个字面中,模板复制在50个上优于生成模型。风格迁移效果仅在包含被排除家族训练数据的内部嵌入空间中可测量,故结果需谨慎解读。本研究贡献包括布局复用范式与全流程、九种文字的审计与基准,以及失败条件、目标选择与参考引导重制的数据边界清单。

原文摘要 · Abstract (English)

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.

字体生成OpenType多语言风格迁移

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