Sign Language Digit Detection with MediaPipe and Machine Learning Algorithm

Abstract

A major challenge when developing Machine Learning (ML) sign language recognition using wearable is how to efficiently translate the gestures based on the acquired sensors data. Conventional method utilizes data fusion based on the obtained sensors' information by producing mapping/lookup table for creating classification model of gestures corresponding sensor value. Although this method is effective, it increases programming complexity. Therefore, emerging technology that can improve the simplicity and provide accuracy of gestures' data processing is needed. This work experiments the artificial intelligence approach of the development of American Sign Language (ASL) detection using MediaPipe, a ready-to-use cross-platform machine learning framework for computer vision works and Google Teachable Machine a free web tool of machine learning model creation.