LoGAN: Multilingual Font Localization with Generative Agents

• URL: http://arxiv.org/abs/2609.07029v1
• Date: Mon, 07 Sep 2026 04:34:15 GMT
• Title: LoGAN: Multilingual Font Localization with Generative Agents
• Abstract summary: We propose LoGAN, a VLM-based framework for few-shot multilingual font localization.<n>LoGAN takes in a small number of individual glyphs from a font or letters from a logo and uses them to generate complete character sets in other languages.<n>We evaluate our approach on both font and real-world logo datasets spanning more than 27 languages.
• Score: 11.01687899097962
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Localizing a font into new languages is a highly intricate task requiring precise design adaptation of glyphs, color/texture, and spacing/kerning, from source to target languages. Most existing methods focus on single glyph generation with limited capability in handling multilingual font rendering. In this work, we propose LoGAN, a VLM-based agentic framework for few-shot multilingual font localization, which takes in a small number of individual glyphs from a font or letters from a logo and uses them to generate complete character sets in other languages. LoGAN breaks down this task into multiple components: a glyph-level diffusion model, a style finetuning module, a spacing and kerning transfer algorithm, and a texture expansion model, with a VLM agent coordinator. LoGAN achieves broad language coverage for font localization with various styles, including Chinese/Korean/Japanese (CJK). We evaluate our approach on both font and real-world logo datasets spanning more than 27 languages and compare it against both specialized font generation and state-of-the-art image editing models with strong text rendering capabilities (e.g., FLUX, Nano-Banana). Our approach yields higher glyph fidelity while maintaining better style, texture, and kerning consistency according to both quantitative and qualitative evaluations.

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