Blame Errant Words, Not the Canvas: Defect-Aware Spatial Credit Assignment for Visual Text Generation

The paper introduces TextDART, a defect-aware reward model that evaluates individual text regions for semantic correctness and glyph quality, and SCANNER, a method that uses these regional judgments to provide spatially differentiated policy feedback. This approach addresses limitations of previous reinforcement learning methods that overlook glyph defects and obscure regional quality. Experiments on existing benchmarks and the new PolyText-Bench show state‑of‑the‑art performance in text accuracy and glyph fidelity.

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• reinforcement-learning
• benchmarks