## 2 Related Works  
OSV has been widely researched and applied in the past decades, and the review articles [1; 2; 3; 4; 5] provide insights into the advantages and limitations of existing methods, emphasizing opportunities for further research and development. With the continuous development of technology, more and more signature datasets are publicly released, such as CEDAR [6], MCYT-75 [7], BHSig260 [8], GPDS [9; 10], and so on. These datasets not only promote the research and development in the field of OSV but also improve the application level of OSV technology. However, there is no large-scale ethnic people signature dataset, and there are certain difficulties in the research and application of OSV for ethnic people. Therefore, we need a new dataset to satisfy this demand. Classic manual feature extraction methods, such as LBP, HOG, and GLCM, are usually used for OSV [11; 12; 13; 14]. However, these handcrafted features are prone to problems in the face of noise and complex background interference and require a lot of time and effort to adjust. Therefore, they perform poorly when applied to some complex data. To address the limitations of traditional handcrafted feature extraction methods in signature verification, researchers commonly employ deep learning techniques. For instance, [PERSON] et al. [15] proposed a Siamese CNN network based on contrastive loss for OSV in their study. [PERSON] et al. [16] proposed a layered CNN for feature learning from signature patches. [PERSON] et al. [17] proposed an Inverse Identification Network (IDN), which focuses on the characteristics of signature strokes. Recent work includes topological graphs [18], Siamese networks [19], deep metric learning for regions [20], RNN networks [21], graph neural network methods [22], models based on Capsule and CNN networks [23], and the encoder-decoder architecture approach [24]. By adopting deep learning methods, it can be widely applied in fields such as forensic identification and finance to improve the progress of issuing certifications and reduce work intensity. However, most of the current OSV methods only use a single or a small number of language signature datasets for model training and do not fully model multi-language signatures, which may lead to poor performance in multi-language signature verification. Therefore, the further research and development of deep learning methods capable of modeling multilingual signatures are required to improve the accuracy and reliability of signature verification. We propose a verification method for multilingual signature datasets spanning regions and ethnic groups, which fuses the ResNet18 [25] and the CBAM [26]. By adopting the ResNet18 network, we can enhance the model's generalization ability to better handle the differences between different languages. In addition, combined with the CBAM, the method is more robust and adaptable to the variability of the signature itself. In modern computing applications, deep learning models have emerged as a critical technology. However, due to the constraints of computational resources, many devices are unable to support large models, thereby hindering the deployment of deep learning models on these devices. To address this issue, researchers have proposed model compression techniques, which employ a variety of methods to reduce the size and computational complexity of the models, enabling deep learning models to be applied on resource-constrained devices. These methods include weight pruning, low-rank decomposition, quantization, and knowledge distillation, which can effectively reduce the model size, accelerate computation, and have a minimal impact on model performance. Consequently, model compression techniques have become a viable means of enabling the application of deep learning models on resource-constrained devices. Researchers have made significant efforts to accelerate convolutional neural networks, which can be categorized into three types: optimization implementation [27], quantization [28], and structural simplification [29].