Ship License Plate Recognition (SLPR) technology serves as a fundamental technological foundation for maritime transportation management. Automated ship identification enhances both regulatory oversight and operational efficiency. However, current recognition models demonstrate significant limitations, including their inability to detect objects in complex environments and challenges in maintaining real-time performance while ensuring accuracy, thereby limiting their practical applicability. This study proposes a novel cascaded framework that integrates RT-DETR-based detection with OCR capabilities. The framework incorporates several key methodological innovations: optimizing the RT-DETR backbone through efficient partial convolutions during training to improve computational efficiency; implementing Conv3XC to modify the ResNet18-backbone BasicBlock using a triple convolutional layer configuration with an enhanced RepC3 kernel design for better feature extraction; and integrating learned position encoding (LPE) to improve the AIFI position encoding mechanism, thereby enhancing detection capabilities. After region detection, PP-OCRv3 is used for character recognition. Experimental results demonstrate the superior performance of our approach: Ship-RT-DETR achieves 96.2% detection accuracy with a 28.5% reduction in parameters and 67.3 FPS, while PP-OCRv3 achieves 91.6% recognition accuracy. Extensive environmental validation across diverse weather conditions (sunny, cloudy, rainy, and foggy) confirms the framework’s robustness, maintaining a detection accuracy above 90% even in challenging foggy conditions, with minimal performance degradation (a 7.7% decrease from optimal conditions). The system’s consistent performance across various environmental conditions (detection standard deviation: 2.84%, OCR confidence standard deviation: 0.0295) establishes a novel and robust methodology for practical SLPR applications.