{"ID":3083846,"CreatedAt":"2026-06-05T06:46:15.197025399Z","UpdatedAt":"2026-06-07T05:32:54.120957816Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.05785","arxiv_id":"2606.05785","title":"Next-Generation Parallel Decoder for LPDR: Architectural Optimization and Class-Balanced GAN-Augmentation","abstract":"Real-Time License Plate Detection and Recognition (LPDR) forms the backbone of modern smart cities. Although the YOLOV5-PDLPR model substantially improved system efficiency through a parallel decoder approach, its performance is still affected by spatial character mismatches and data imbalance within the training set. This paper addresses these limitations by introducing Cross-Spatial Hybrid Attention (CSHA) and Class-Balanced Synthetic Augmentation (CBSA). An extensive study involving 75,000 synthetic samples is conducted and evaluated on four benchmarks: CCPD, CLPD, PKU, and an application-specific dataset. Experimental results demonstrate a substantial improvement in the recognition rate of minority provincial license plates from 78.2% to 91.5% while maintaining real-time processing performance of 152 FPS. The results indicate that spatially-aware parallel decoding combined with class-balanced augmentation provides an effective solution for high-speed license plate recognition systems.","short_abstract":"Real-Time License Plate Detection and Recognition (LPDR) forms the backbone of modern smart cities. Although the YOLOV5-PDLPR model substantially improved system efficiency through a parallel decoder approach, its performance is still affected by spatial character mismatches and data imbalance within the training set....","url_abs":"https://arxiv.org/abs/2606.05785","url_pdf":"https://arxiv.org/pdf/2606.05785v1","authors":"[\"Shawaiz Obaid\",\"Nida Chandio\",\"Neha Jamil\",\"Muhammad Khuram Shahzad\"]","published":"2026-06-04T07:16:06Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\",\"cs.LG\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}