Deep Learning-Based Malaysian Sign Language (MSL) Recognition: Exploring the Impact of Color Spaces

Sign language is one form of communication for this group of people to communicate with each other. Not only for people with hearing problems but sign language is also useful for people who are mute or have problem speaking. The most used sign language is the American Sign Language (ASL) that is widely used in English speaking countries. In Malaysia, Bahasa Isyarat Malaysia (BIM) or Malaysian Sign Language (MSL) is still a new teaching to the community in Malaysia. Hence, in this project, a dataset with 5980 images of the signed alphabet is used to train models to recognize what the signs mean. The problem this project aims to address is the limited research and the availability of datasets in the field of Malaysian Sign Language (MSL) recognition using deep learning and various color spaces. Two deep learning models that are used are Convolutional Neural Network (CNN) and Residual Network 18 (ResNet18). The images are also converted into different color spaces which are RGB, YCbCr, Grayscale and the combination of RGB and YCbCr. The findings reveal that RGB is the most effective color space for CNNs, achieving up to 83.9% accuracy, while YCbCr performed best with ResNet18, achieving 88.3% accuracy. These results demonstrate the importance of color space selection in sign language recognition and contribute to the growing body of research on MSL. Key metrics such as precision, recall, and F1-score further underscore the robustness of the proposed system.