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With the rapid advancement of urban intelligence, there is an increasingly urgent demand for technological innovation in traffic management. License plate recognition technology can achieve high accuracy under ideal conditions but faces significant challenges in complex traffic environments and adverse weather conditions. To address these challenges, we propose the enhanced attention network and decoding for license plate recognition model (EAND-LPRM). This model leverages an encoder to extract features from image sequences and employs a self-attention mechanism to focus on critical feature information, enhancing its capability to handle complex traffic scenarios such as rainy weather and license plate distortion. We have curated and utilized publicly available datasets that closely reflect real-world scenarios, ensuring transparency and reproducibility. Experimental evaluations conducted on these datasets, which include various complex scenarios, demonstrate that the EAND-LPRM model achieves an accuracy of 94%, representing a 6% improvement over traditional license plate recognition algorithms. The main contributions of this research include the development of a novel attention-mechanism-based architecture, comprehensive evaluation on multiple datasets, and substantial performance improvements under diverse and challenging conditions. This study provides a practical solution for automatic license plate recognition systems in dynamic and unpredictable environments. [ABSTRACT FROM AUTHOR]

With the rapid advancement of urban intelligence, there is an increasingly urgent demand for technological innovation in traffic management. License plate recognition technology can achieve high accuracy under ideal conditions but faces significant challenges in complex traffic environments and adverse weather conditions. To address these challenges, we propose the enhanced attention network and decoding for license plate recognition model (EAND-LPRM). This model leverages an encoder to extract features from image sequences and employs a self-attention mechanism to focus on critical feature information, enhancing its capability to handle complex traffic scenarios such as rainy weather and license plate distortion. We have curated and utilized publicly available datasets that closely reflect real-world scenarios, ensuring transparency and reproducibility. Experimental evaluations conducted on these datasets, which include various complex scenarios, demonstrate that the EAND-LPRM model achieves an accuracy of 94%, representing a 6% improvement over traditional license plate recognition algorithms. The main contributions of this research include the development of a novel attention-mechanism-based architecture, comprehensive evaluation on multiple datasets, and substantial performance improvements under diverse and challenging conditions. This study provides a practical solution for automatic license plate recognition systems in dynamic and unpredictable environments.