Perception or Prior? Legibility-Guided Path Selection for Text Image Restoration in the Wild

Text image restoration requires preserving character identity and stroke geometry. Recognition-guided methods exploit character priors but are limited by vocabulary coverage. However, visible stroke patterns can guide reconstruction even for out-of-vocabulary characters, while reliable priors can complement visual evidence under severe degradation. Motivated by this complementarity, we propose GRASP (Glyph Reconstruction via Adaptive Selection of Paths), a framework for legibility-guided selection between visual reconstruction and character-prior completion. A geometry-aware extractor aligns character features in a shared glyph space, where a visual path reconstructs observed structures and a prior path generates character-conditioned glyphs with input-dependent style modulation. A selector uses visual evidence and recognition confidence to choose complete glyphs, with visual fallback for predictions labeled as unknown. The selected glyphs form a structure mask for full-image restoration. We develop a synthetic training pipeline spanning 900 fonts and introduce MText500, a multilingual real-world benchmark. Experiments demonstrate the effectiveness of GRASP across character subsets and degradation levels, including languages absent from full-image restoration training.