Multi-View Occluded License Plate Recognition: A Feature Fusion Method Based on Spatial Wavelet Transform

License plate recognition is a critical perception module in intelligent transportation systems, yet severe occlusion remains a fundamental challenge in unconstrained traffic scenes because the missing visual evidence is physically absent rather than merely degraded. Although multi-view observations provide complementary cues for recovering occluded characters, existing fusion strategies usually operate in the spatial domain and therefore suffer from feature misalignment, especially when Vision Transformers rely on local patch-wise positional encoding. To address this issue, we propose Wavelet-Enhanced Transformer, a domain-transformed multi-view recognition framework for occluded license plates. The central idea is to shift feature fusion from the spatial domain to the wavelet domain, where discrete wavelet transform decomposes encoded features into low-frequency structural components and high-frequency detail components. This transformation alleviates spatial semantic ambiguity caused by viewpoint variation and positional mismatch, enabling more robust cross-view feature aggregation. On top of this representation, we design an order-agnostic memory fusion mechanism to progressively accumulate complementary evidence from multiple partial observations, and introduce a discriminator-based stopping module to adaptively terminate unnecessary iterations for efficient deployment. Experiments on the CBLPRD-330k dataset and severe occlusion stress tests demonstrate the superiority of the proposed method. Under 20–50% character occlusion, our model maintains over 95% character accuracy and consistently outperforms mainstream baselines such as LPRNet, TrOCR, and PP-OCRv5 in character accuracy, length accuracy, and full-match rate (FMR). These results indicate that wavelet-domain feature fusion provides an effective and robust solution for highly occluded license plate recognition in complex traffic environments.