GlyphPrinter: Region-Grouped Direct Preference Optimization for Glyph-Accurate Visual Text Rendering
Xincheng Shuai, Ziye Li, Henghui Ding, Dacheng Tao

TL;DR
GlyphPrinter is a novel text rendering method that uses region-based preference optimization to improve glyph accuracy without relying on explicit reward models, effectively handling complex and out-of-domain characters.
Contribution
It introduces Region-Grouped DPO and a new dataset for region-level preferences, advancing glyph accuracy in visual text rendering beyond existing approaches.
Findings
Outperforms existing methods in glyph accuracy
Balances stylization and precision effectively
Utilizes region-level preferences for localized improvements
Abstract
Generating accurate glyphs for visual text rendering is essential yet challenging. Existing methods typically enhance text rendering by training on a large amount of high-quality scene text images, but the limited coverage of glyph variations and excessive stylization often compromise glyph accuracy, especially for complex or out-of-domain characters. Some methods leverage reinforcement learning to alleviate this issue, yet their reward models usually depend on text recognition systems that are insensitive to fine-grained glyph errors, so images with incorrect glyphs may still receive high rewards. Inspired by Direct Preference Optimization (DPO), we propose GlyphPrinter, a preference-based text rendering method that eliminates reliance on explicit reward models. However, the standard DPO objective only models overall preference between two samples, which is insufficient for visual text…
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Taxonomy
TopicsComputer Graphics and Visualization Techniques · Generative Adversarial Networks and Image Synthesis · Handwritten Text Recognition Techniques
