arXiv Machine Learning
Srijika: OpenType-Layout-Reusing Font Restyling for Nine Indic Scripts
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NIV: Neural Axis Variations for Variable Font Generation
arXiv:2606. 05261v1 Announce Type: cross Abstract: Variable fonts enable continuous variation of glyph geometry along semantic design axes such as weight, width, slant, and optical size.
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LoGAN: Multilingual Font Localization with Generative Agents
LoGAN is a VLM-based agentic framework designed for few-shot multilingual font localization. It takes a handful of glyphs or logo letters and generates complete character sets across many languages, including CJK, by combining a glyph-level diffusion model, style finetuning, spacing/kerning transfer, and texture expansion. The method outperforms specialized font generators and state‑of‑the‑art image editors in glyph fidelity, style, texture, and kerning consistency on datasets covering more than 27 languages.
arXiv AI
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Persian Pixel: A large-scale synthetic OCR dataset for Persian language
arXiv:2607. 20385v1 Announce Type: cross Abstract: Optical Character Recognition (OCR) for Persian remains substantially less mature than for Latin-script languages despite Persian being spoken by more than 110 million people across multiple countries.
arXiv Computation and Language
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Evaluating In-Context Learning and Retrieval Strategies for Devanagari Post-OCR Correction
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arXiv Computer Vision
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MSTypography: Multi-character Semantic Typography via Balancing Word Legibility and Object Recognizability
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arXiv AI
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When Explanations Cannot Be Read: Measuring and Correcting SHAP and LIME Rendering for Right-to-Left Languages
The paper "When Explanations Cannot Be Read: Measuring and Correcting SHAP and LIME Rendering for Right-to-Left Languages" identifies that standard SHAP and LIME visualizations, designed for left‑to‑right scripts, fail to display attribution values correctly for right‑to‑left languages such as Urdu, Arabic, Persian, and Hebrew. It introduces SHAP‑RTL, a rendering layer that preserves the original attribution values while correcting reading direction, script shaping, and font selection for each language. The authors evaluate SHAP‑RTL on hate‑and‑offensive‑language datasets using TF‑IDF and logistic regression, showing that default rendering yields high character error rates, while SHAP‑RTL maintains correct visualizations across all tested languages.