TextPecker is a structural anomaly perceptive model designed to enhance Visual Text Rendering (VTR). It addresses a critical bottleneck where standard MLLMs and OCR models fail to perceive structural anomalies such as distortion, blurriness, and misalignment in generated text. This model acts as a plug-and-play evaluator and reward signal for RL-based optimization (e.g., using Flow-GRPO), enabling the generation of structurally faithful visual text.
This checkpoint is built upon the
Qwen3-VL-8B-Instruct architecture and was trained using
ms-swift.
TextPecker can be used to evaluate text structural quality and semantic consistency for text-to-image generation or editing tasks. It is particularly useful for:
To use this model, please follow the official deployment and testing instructions:
1@article{zhu2026TextPecker,
2 title = {TextPecker: Rewarding Structural Anomaly Quantification for Enhancing Visual Text Rendering},
3 author = {Zhu, Hanshen and Liu, Yuliang and Wu, Xuecheng and Wang, An-Lan and Feng, Hao and Dingkang Yang and Chao Feng and Can Huang and Jingqun Tang and Xiang Bai},
4 journal = {arXiv preprint arXiv:2602.20903},
5 year = {2026}
6}
