TextPecker: Rewarding Structural Anomaly Quantification for Enhancing Visual Text Rendering
Visual Text Rendering (VTR) remains a critical challenge in text-to-image generation, where even advanced models frequently produce text with structural anomalies such as distortion, blurriness, and misalignment. However, we find that leading MLLMs and specialist OCR models largely fail to perceive these structural anomalies, creating a critical bottleneck for both VTR evaluation and RL-based optimization. As a result, even state-of-the-art generators (e.g., SeedDream4.0, Qwen-Image) still struggle to render structurally faithful text. To address this, we propose TextPecker, a plug-and-play structural anomaly perceptive RL strategy that mitigates noisy reward signals and works with any textto-image generator. To enable this capability, we construct a recognition dataset with character-level structural-anomaly annotations and develop a stroke-editing synthesis engine to expand structural-error coverage. Experiments show that TextPecker consistently improves diverse text-to-image models; even on the well-optimized Qwen-Image, it significantly yields average gains of 4% in structural fidelity and 8.7% in semantic alignment for Chinese text rendering, establishing a new state-of-the-art in high-fidelity VTR. Our work fills a gap in VTR optimization, providing a foundational step towards reliable and structural faithful visual text generation.
699ea351dfbcf0b800aecc1a
Hanshen Zhu
claimed_verified
2026-02-25T17:28:42.893Z
662213b3c819b7ce3d41f987
https://cdn-avatars.huggingface.co/v1/production/uploads/662213b3c819b7ce3d41f987/cSdLQy5YbbaTokzKXMtrS.jpeg
Hanshen Zhu
CIawevy
user
CIawevy
699ea351dfbcf0b800aecc1b
Yuliang Liu
699ea351dfbcf0b800aecc1c
Xuecheng Wu
699ea351dfbcf0b800aecc1d
An-Lan Wang
699ea351dfbcf0b800aecc1e
Hao Feng
699ea351dfbcf0b800aecc1f
Dingkang Yang
699ea351dfbcf0b800aecc20
Chao Feng
699ea351dfbcf0b800aecc21
Can Huang
699ea351dfbcf0b800aecc22
Jingqun Tang
699ea351dfbcf0b800aecc23
Xiang Bai
TextPecker: Rewarding Structural Anomaly Quantification for Enhancing Visual Text Rendering
Visual Text Rendering (VTR) remains a critical challenge in text-to-image generation, where even advanced models frequently produce text with structural anomalies such as distortion, blurriness, and misalignment. However, we find that leading MLLMs and specialist OCR models largely fail to perceive these structural anomalies, creating a critical bottleneck for both VTR evaluation and RL-based optimization. As a result, even state-of-the-art generators (e.g., SeedDream4.0, Qwen-Image) still struggle to render structurally faithful text. To address this, we propose TextPecker, a plug-and-play structural anomaly perceptive RL strategy that mitigates noisy reward signals and works with any textto-image generator. To enable this capability, we construct a recognition dataset with character-level structural-anomaly annotations and develop a stroke-editing synthesis engine to expand structural-error coverage. Experiments show that TextPecker consistently improves diverse text-to-image models; even on the well-optimized Qwen-Image, it significantly yields average gains of 4% in structural fidelity and 8.7% in semantic alignment for Chinese text rendering, establishing a new state-of-the-art in high-fidelity VTR. Our work fills a gap in VTR optimization, providing a foundational step towards reliable and structural faithful visual text generation.
TextPecker enhances visual text rendering by addressing structural anomalies through a reinforcement learning approach that improves text-to-image generation quality and fidelity.
visual text rendering text-to-image generation structural anomalies reinforcement learning textPecker character-level annotations stroke-editing synthesis structural fidelity semantic alignment