VIETNAMESE SIGN LANGUAGE RECOGNITION USING MEDIAPIPE AND SVM FOR ASSISTIVE COMMUNICATION SYSTEMS FOR THE HEARING-IMPAIRED

Sign Sign language serves as an essential means of communication for the hearing-impaired community. However, access to and adoption of sign language in Vietnam remain limited due to a lack of resources and supporting tools. To address this issue, this study proposes a Vietnamese Sign Language recognition system that combines MediaPipe technology with the Support Vector Machine (SVM) classification algorithm. The training dataset is constructed from hand gesture images, with MediaPipe responsible for detecting and extracting hand landmark features. These features are then classified using the SVM model. Experimental results demonstrate that the system achieves an accuracy rate between 85% and 90%, confirming its potential to support communication for hearing-impaired individuals through sign language recognition technology.