## 1 Introduction  
Handwritten signature verification is the process of verifying the authenticity, legitimacy, and validity of handwritten signatures. All over the world, a large number of important financial, commercial, and judicial documents are signed every day, so verifying signatures has become a critical issue that requires attention. Considering the widespread use and the huge number of handwritten signatures, it has become particularly urgent and necessary to develop an automatic, accurate, and efficient signature verification technology. Offline Signature Verification (OSV) requires the signature images to be recorded in advance. Then, pattern recognition, computer vision, image processing, and other technologies are used to extract the signature's characteristic information for analysis and comparison in order to determine the authenticity of the signature.  
Although OSV has made remarkable progress over the past few decades, there are still some challenges that need to be solved. First, there is a lack of public, offline handwritten signature datasets for ethnic people such as Uyghur, Kazakh, and Kirgiz research and applications for these ethnic peoples' signatures. Second, the diversity of signatures requires OSV algorithms to have the ability to generalize across different regions and nationalities so as to adapt to the application needs of different scenarios and user groups. Most people have a lot of variability in their signature styles, making it difficult for algorithms to correctly verify different signatures from the same person. Third, modern deep learning models usually have a large number of parameters, which require a lot of memory and computing resources to store and process. This will not only slow down the calculation speed of the model but also increase the storage and calculation burden of the model in resource-constrained environments such as mobile devices.  
The primary achievements of our research can be outlined as follows:  
1. First, we constructed a signature dataset containing Uyghur, Kazakh, Kirgiz, and Chinese signatures. This dataset consists of 38,400 signature images from 200 individuals of each ethnic group;
2. In this study, we conducted experiments on a signature dataset spanning different regions and ethnicities using the ResNet18 network. Furthermore, we introduced the Convolutional Block Attention Module (CBAM) to enhance the model's generalization ability. The CBAM can adaptively adjust the weight and scale of feature maps so that the network can better adapt to different signature styles and features. This experiment proves that the CBAM is robust and adaptable to the diversity and variability of the signature itself;
3. By compressing the model, we have successfully reduced its storage and computational costs while maintaining high accuracy. This achievement provides an excellent solution for the application of the model to resource-constrained devices, such as embedded and mobile devices. As a result, our model can be widely used in various scenarios where resources are limited.  
In the field of signature verification, due to the lack of public ethnic people datasets for research, we collected a large-scale offline handwritten signature dataset that includes Chinese, Uyghur, Kazakh, and Kirgiz. We tested our method on both our self-built dataset and public datasets. Through experiments, it is proved that our method has strong effectiveness and generality in the verification of cross-language signature datasets.