MVR: Diffusion-Based Multi-View Reasoning for Scene Text Detection

Scene text detection in real-world environments often suffers from view-dependent failures, where text instances are visible in one view and are missed in other views due to perspective distortion, occlusion, and appearance variation. Although multi-view imagery provides complementary information, most existing methods process each view independently and lack explicit mechanisms to utilise cues in individual views for detecting text in multi-view scenarios accurately. In this paper, we propose a diffusion-based method for robust multi-view scene text detection in real-world environments. Given multiple views of the same scene, we first identify text instances that are detected in some views but absent in others through cross-view association. We then propose a diffusion-based recovery framework that reconstructs missing text bounding boxes by leveraging geometric consistency across supporting views. Missing detections are modelled as latent variables conditioned on multi-view context, enabling principled recovery rather than heuristic fusion. We design a detector-agnostic, multi-stage pipeline consisting of frozen single-view text detection, cross-view instance association, base-box generation from detected views, and residual diffusion-based refinement. The diffusion model learns to denoise residual offsets between recovered base boxes and ground-truth boxes, allowing accurate localization without retraining the underlying detector. Experiments on multi-view street-level scenes demonstrate that the proposed approach recovers a large fraction of text instances missed by single-view detectors, yielding substantial improvements in missing-view recall. These results highlight the effectiveness of diffusion models for robust multi-view scene text detection in real-world environments. The proposed method is also tested on benchmark datasets, namely, ICDAR 2015, Total-Text and CTW1500, to demonstrate the effectiveness of the method. The dataset is available at MVSD.