## 6 Conclusions  
The application of deep learning methods in OSV faces many challenges, among which the diversity and variability of signatures make it difficult for algorithms to correctly verify different signatures of different user groups or the same person. To address these challenges, we propose a new OSV model that aims to improve the accuracy while making the model lightweight. Due to the lack of public, offline handwritten signature datasets for ethnic people, we collected a large-scale signature dataset that includes genuine and forged signature images in Chinese, Uyghur, Kazakh, and Kirgiz. We used ResNet18 as the base model, which is a classic residual network model with a smaller model size and better performance. Although the ResNet18 model has the advantages of ease of implementation and training, it does not perform well for the diversity and variability of signature datasets spanning different languages and ethnicities. The contribution of this paper is that, by introducing the CBAM, the relationship between different feature channels and spatial locations is better captured, thus improving the adaptability of the model to different signature styles and features. Meanwhile, we compressed the model and successfully reduced its storage and computational costs while maintaining high accuracy. The evaluation results show that our FC-ResNet models exhibit significant advantages on both self-built and public datasets, thus demonstrating the strong effectiveness and generality of our method on cross-lingual datasets. In future work, we will aim to further expand our datasets, optimize our model, and apply it to a wider range of domains for more accurate and robust signature verification.  
Conceptualization, Y.M.; methodology, Y.M.; software, Y.M.; investigation, Y.M.; validation, Y.M.; resources, M.M., N.Y. and K.U.; writing--original draft preparation, Y.M. and A.A.; writing--review and editing, Y.M. and A.A.; visualization, Y.M.; project administration, M.M., N.Y. and K.U.; funding acquisition, A.A. and K.U. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (No. 62266044, 62061045, 61163028, 61862061) and the Science and Technology Plan Project of Xinjiang Uyghur Autonomous Region, China (2021D01C119).  
Not applicable.  
The self-built datasets used in this paper, Chinese, Uyghur, Kazakh and Kirgiz datasets, have not been made public. This is the dataset collected by our college, which will be published soon.  
The authors are very thankful to the editor and the referees for their valuable comments and suggestions for improving the paper.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Authors** & **Individual** & **Method** & \
\begin{tabular}{c} **FAR** \\ **(\%)** \\ \end{tabular} & **FRR (\%)** &
\begin{tabular}{c} **ACC** \\ **(\%)** \\ \end{tabular} \\ \hline [PERSON] et al. [17] & 749 & IDN & 11.52 & 5.47 & 90.17 \\ [PERSON] et al. [35] & 1243 & STN & 10.21 & 10.21 & 89.79 \\ [PERSON] et al. [20] & 1243 & DenseNet-36 & - & - & 90.09 \\ [PERSON] et al. [38] & 100 & IDN + SE + ESA & 4.46 & 5.2 & 95.17 \\ Ours & 200 & FC-ResNet & 3.44 & 3.13 & 96.72 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison on the Chinese signature dataset.