## 1 Introduction  
In a recent 2023 study by the World Health Organization (WHO), it was reported that approximately 466 million people are living with disabling hearing loss caused by various factors [1]. Experts estimate that by 2050, hearing loss will affect 700 million people worldwide, which is equivalent to one in ten individuals. A parallel report by JapanTrak [2] suggested that by 2022, 10 percent of Japan's population, 10 million people, will experience hearing loss. A survey conducted by the Committee for the Promotion of Hearing Loss Assistance Programs [2] revealed that about 70% of individuals with hearing loss have experienced job changes because of challenges in relationship building and requesting accommodations.  
Advances in deep learning technology have been instrumental in propelling research aimed at enhancing communication with individuals who are deaf or having difficulty hearing. Over the past decade, this technology has seen significant advancements, with research in deep learning-based sign language recognition garnering substantial attention. Compared to traditional video recognition, sign language recognition poses unique challenges. The RGB-based recognition requires an extensive number of samples because of the minimal correlation between sign language movements and background, coupled with limited inter-frame variation. The perceivability of sign language movements is influenced by various factors, such as the signer's speed, body shape, rhythm, and performance location, underscoring the necessity for models exclusively focusing on sign language movements.  
Recently, skeleton-based sign language recognition methods have received increasing attention owing to their adaptability in dynamic scenarios and against complex backgrounds. Researchers have recently shifted their focus to developing several skeleton-based approaches [3; 4] that incorporate diverse types of feature data into coordinate values derived from pose estimation and categorize them using convolutional neural network (CNN) models.  
We introduce a straightforward and accessible method for recognizing all 46 Japanese finger spellings. Our goal is to reduce computational complexity and perform recognition in real time by making input data two-dimensional through feature extraction. This method employs a visual transformer (ViT) [5] model, which has become a topic of interest in computer vision. The first step of our method involves using a posture estimation library, MediaPipe [6], to capture the coordinates of each finger joint from a video frame. These coordinates are then used to calculate one-dimensional angular feature data. We organize the angular feature data to create a two-dimensional input vector as the next step. This vector is essential for the recognition process, which is conducted using the ViT model. An important part of this process is adjusting the number of encoder layers in the ViT. This change allows us to assess which configuration yields the highest recognition accuracy and identify the optimal number of encoder layers. To show the effectiveness of our proposed method, we will compare its performance with that of existing CNN models. This comparison aims to showcase the potential improvements and benefits of using the ViT model for sign language recognition, especially in Japanese sign language.