## 1 Introduction  
Recent advancements in information and communication technology have brought positive changes to document management methods across various industries and institutions. Companies, public institutions, and financial organizations can reduce the spatial limitations of physical document storage and improve the efficiency of document transmission and management by digitizing important documents. However, with the development of image processing and document editing tools, digital documents are now exposed to forgery and manipulation. A wide range of digital documents, from everyday documents to critical ones handled by key institutions, can become targets for forgery and manipulation. If these forged documents are distributed uncontrollably, they can cause serious social disruption. For example, the distribution of forged legal documents or contracts can lead to significant legal disputes, while manipulated financial documents can result in economic damage. As a result, many countries are actively conducting research on detecting forgery in digital documents. Recently, deep learning technologies have gained attention for their high accuracy in digital document forgery detection. For instance, [PERSON] et al. 's method [1] combines convolutional neural networks (CNNs) with background pattern analysis and template synthesis to assess whether a document conforms to its original format. Similarly, [PERSON] et al. 's method [2] introduces an unsupervised approach that utilizes hyperspectral imaging; in this method, a convolutional autoencoder (CAE) extracts the spectral features of inks, and a logistic regression classifier is employed to effectively distinguish between different inks, thereby detecting subtle alterations in the document. Since deep learning models such as CNNs require large amounts of training data for forgery detection, it is crucial not only to develop sophisticated models but also to create diverse datasets that can be used in developing new deep learning models. However, the majority of datasets developed and used in this field are based on English documents, which limits their effectiveness in detecting forgery across different languages. For example, after reviewing ten papers published since 2023 on the topic of document forgery detection ([3; 4; 5; 6; 7; 8; 9; 10; 11; 12]), it was found that nine out of these ten studies conducted their research using English-based datasets. Example images of the datasets used in [3] and [4] are shown in Figure 1. In addition, [13] introduced six datasets (CVL [14], ICDAR 2011 [15], KHATT [16], ICPR2018 [17], IMEI number [18], Google-LIFE-Magazine [19]) used for digital document forgery detection, five of which are based on the English alphabet. As these examples show, the majority of datasets published in the field of digital document forgery detection are in English. However, deep learning-based digital document forgery detection models, which achieve high performance with English-based data, may show different accuracy when applied in cultures that do not use English. This  
Figure 1: Examples of English-based datasets: (a) receipt dataset and (b) DocTamper dataset. discrepancy arises because the models are trained on English data but applied to data in another language. Each language has its own structural complexity, so in order to improve detection accuracy, datasets that reflect the characteristics of the target language are needed. For instance, Korean and English have significant differences in their writing systems, and these differences can affect the accuracy of forgery detection algorithms. The visual complexity of a script plays an important role in cognitive processing, and languages with more complex scripts may require greater cognitive resources. This variation in complexity can lead deep learning models to process Korean and English documents differently when detecting forgery. If this is not taken into account during model training, the detection accuracy may suffer [20].