## 1 Introduction
In order to reduce the influence of external factors on license plate recognition, [PERSON] et al. [7] present Adaptive Feature Attention Network (AFA-Net), an adaptive feature attention network for license plate recognition (LPR) that addresses low-resolution and motion blur issues in dash cam images, significantly improving LPR performance. They also introduce Joint-Image Restoration and License Plate Recognition Network (Joint-IRLPRNet), which further enhances performance by simultaneously restoring and recognizing license plates. In order to improve the license plate recognition rate in special scenarios, [PERSON] et al. [8] proposed a generative adversary network combining image super resolution and the deblur method with the discrete cosine transform discriminator. The Discrete Cosine Transform (DCT) with low computational complexity is used to remove various types of ambiguity and complexity from the license plate, and achieve better recognition of license plate images in a complex environment. The research mentioned above highlights that, under ideal conditions and specific circumstances, license plate recognition demonstrates commendable accuracy. However, existing methods falter when confronted with multiple complex real-world traffic scenarios, resulting in subpar license plate recognition performance [9]. For instance, factors such as deteriorating image quality due to weather changes, uneven license plate brightness caused by lighting conditions, poor dataset quality, blurriness in license plate quality due to high vehicle speeds, and license plate deformation caused by varying capture angles present considerable challenges to both the robustness and accuracy of recognition processes [10]. To solve the above problems, we propose the enhanced attention network and decoding for license plate recognition model (EAND-LPRM). This model specifically targets the difficulties presented by complex traffic scenarios by leveraging an encoder to extract deep features from image sequences and employing a self-attention mechanism to focus on critical feature information. This adaptation is essential to tackle the complexities of real-world traffic scenarios effectively, demanding continuous refinement and enhancement in the field. Regarding the issues mentioned above, this paper makes significant contributions in two main aspects. * We integrated various publicly available datasets and meticulously curated a diverse collection of license plate images encompassing a wide range of scenarios and categories. The selection criteria for these datasets were based on their ability to provide a comprehensive representation of real-world conditions. Specifically, our criteria focused on encompassing different weather conditions such as heavy rain and dense fog, which result in low visibility, as well as addressing challenges such as license plate deformation due to varying capture angles and obstruction of plate information due to occlusions. Additionally, we included datasets capturing images under challenging lighting conditions to reflect scenarios with poor visibility caused by inadequate illumination. This comprehensive approach better reflects the complex situations encountered in traffic scenes and represents real-world traffic scenarios more accurately. By incorporating datasets that specifically cater to these conditions, we aimed to equip our model with a stronger ability to cope with special traffic scenarios. This strategy enhances the robustness and adaptability of our model, ensuring that it performs optimally across a wide range of challenging conditions commonly encountered in practical applications. * We proposed the EAND-LPRM (enhanced attention network and decoding for license plate recognition model) network model, showcasing advanced feature representation capabilities. This model excels in concurrently addressing license plate recognition tasks across various intricate scenarios and challenging weather conditions, thereby optimizing the recognition process.