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

Key points

• We identify two coupled limitations: recognition-based rewards often overlook visible glyph defects, while whole-image scalar feedback obscures the uneven quality of individual text regions.1
• First, we introduce $TextDART$, a defect-aware region-targeted reward model that localizes individual text regions and independently evaluates their semantic correctness and glyph quality, providing fine-grained, region-precise feedback.1
• Building on TextDART, we propose $SCANNER$, which turns these regional judgments into spatially differentiated policy feedback.1
• Extensive experiments on existing benchmarks and our newly established $PolyText-Bench$ demonstrate that our approach achieves state-of-the-art performance in text accuracy and glyph fidelity.1

Sources (1)

• [1]Blame Errant Words, Not the Canvas: Defect-Aware Spatial Credit Assignment for Visual Text Generation
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 06:38 PM

  Recent reinforcement learning approaches have improved visual text generation, yet generated images still suffer from incorrect wording and malformed glyphs.

  We identify two coupled limitations: recognition-based rewards often overlook visible glyph defects, while whole-image scalar feedback obscures the uneven quality of individual text regions.

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