Hangul Character Recognition of A New Hangul Dataset with Vision Transformers Model

This study aims to develop a Vision Transformers (ViT) model for recognizing Korean characters (Hangeul) in response to the growing interest in learning the Korean language and Korean culture in Indonesia. The research methodology involves training the ViT model using a comprehensive dataset of 29,636 base Korean characters. The ViT model has achieved a significant level of accuracy with the score of 93% in recognizing base Korean characters. By integrating deep learning, this study is expected to make a positive contribution to the development of language learning tools for Korean character recognition, unlocking its potential for applications and systems based on the Korean language.