Abdul Sami

Abdul Sami

Researcher at System Software Lab

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publications

Text-Conditioned Diffusion Model for High-Fidelity Korean Font Generation

Published in 2025 International Conference on Information Networking (ICOIN), 2025

DK-Font presents a one-shot, text-conditioned diffusion model that turns a single reference glyph into a full, high-fidelity Korean typeface. By fusing a phonetic-aware text encoder with perceptual loss, the method preserves stroke integrity and style consistency across all 2,350 Hangul characters, boosting SSIM from 0.812 to 0.857 and lowering FID to 10.45 compared with Diff-Font.

Recommended citation: Your Name, You. (2024). "Paper Title Number 3." GitHub Journal of Bugs. 1(3).
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Diffusion-Driven Image Generation with Structural Precision and Style Fidelity

Published in Currently Working...... , 2025

A one-shot denoising diffusion framework that fuses multi-scale style vectors, a Sobel-based structural loss, and CLIP style alignment to generate high-resolution glyphs across Korean, Chinese, and Latin scripts with pixel-level stroke precision and consistent visual style.

Recommended citation: Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2).
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Positional Component-Guided Hangul Font Image Generation via Deep Semantic Segmentation and Adversarial Style Transfer

Published in MDPI Electronics, 2025

We propose a YOLOv8-driven, positional-aware decomposition of Hangul into initial, medial, and final components, then employ a GAN to recombine them, yielding fonts that better preserve structure and style than existing methods such as MXFont and CKFont. This framework boosts accuracy (lower L1/FID, higher SSIM) and opens the door to finely controllable, multilingual typography.

Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1).
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