Srijika: OpenType-Layout-Reusing Font Restyling for Nine Indic Scripts
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 20 references that are in the map. First posted on arXiv 4 Sep 2026.
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References · 13
- 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
- 2022Classifier-Free Diffusion Guidancecited by 5,847
- 2023T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Modelscited by 1,351
- 2019Few-Shot Unsupervised Image-to-Image Translationcited by 222
- 2021DG-Font: Deformable Generative Networks for Unsupervised Font Generationcited by 70
- 2023FontDiffuser: One-Shot Font Generation via Denoising Diffusion with Multi-Scale Content Aggregation and Style Contrastive Learningcited by 48
- 2021Multiple Heads are Better than One: Few-shot Font Generation with Multiple Localized Expertscited by 46
- 2023CF-Font: Content Fusion for Few-shot Font Generationcited by 26
- 2023VecFusion: Vector Font Generation with Diffusioncited by 20
- 2023DualVector: Unsupervised Vector Font Synthesis with Dual-Part Representationcited by 16
- 2024FontCLIP: A Semantic Typography Visual-Language Model for Multilingual Font Applicationscited by 8
- 2023VecFontSDF: Learning to Reconstruct and Synthesize High-quality Vector Fonts via Signed Distance Functionscited by 7
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- 2025FontAdapter: Instant Font Adaptation in Visual Text Generation
- 2024DiffCJK: Conditional Diffusion Model for High-Quality and Wide-coverage CJK Character Generation
- 2026LoGAN: Multilingual Font Localization with Generative Agents
- 2024One-Shot Multilingual Font Generation Via ViT
- 2025MX-Font++: Mixture of Heterogeneous Aggregation Experts for Few-shot Font Generation