Innovative Filipino Sign Language Translation and Interpretation with MediaPipe
Abstract
Filipino Sign Language (FSL) serves as a vital means of communication for the Deaf and hard-of-hearing in the Philippines. However, its societal use remains limited due to the scarcity of qualified interpreters and the general lack of FSL literacy among the population. Therefore, this study aims to address the gap between FSL development and automated FSL translation by employing machine learning and computer vision techniques. A model was trained using the FSL-105 dataset, which comprises video clips of gestures related to greetings and colors, and utilized MediaPipe for real-time detection of hand, face, and body landmarks. Through iterative training with transfer learning, the model’s performance improved from an initial accuracy of 80% to a final accuracy of 98.75%. The results demonstrate that the MediaPipe-based model can reliably interpret FSL gestures, positioning it as a potentially accessible assistive tool for the Deaf and hard of hearing community. This technology holds promise for applications in education, healthcare, and public service, offering new opportunities to promote the social inclusion of Filipino Deaf communities through more inclusive communication.
Citation
Alejo, Z.A.; Fuentes, N.C.J.R.; Lungay, M.P.Z.; Maniquez, A.I.D.; Empas, P.E.G.; Cruz, J.P.T. Innovative Filipino Sign Language Translation and Interpretation with MediaPipe. Eng. Proc. 2026, 134, 75. https://doi.org/10.3390/engproc2026134075
Read the full text
Full-text access is limited to 2 theses per day. Enter your MMCM email to open this study.
Your MMCM email is recorded only for the library’s controlled-access and borrowing records, in line with the Intellectual Property Code. It is not used for anything else.
