A Hand Gesture Recognition System for Deaf-Mute Individuals

Abstract

A deaf-dumb individual always uses gestures to convey his/her ideas to others. However, it is hard for people to understand this gesture language. The purpose of the project is to develop a computer-based system to recognize 26 gestures from American Sign Language (ASL) using MATLAB, which will enable deaf-dumb individuals significantly to communicate with all other people using their natural hand gestures. The proposed system in this project is composed of five modules, which are prepared datasets for ASL which was self-collected using hand gestures from both male and female volunteers, who have alternative ages and skin color in different backgrounds and postures by an ordinary phone camera in total the dataset was 260 images preprocessing, hand segmentation, feature extraction, sign recognition, and text of sign voice conversion. Segmentation is done by converting the image to Hue-Saturation-Value (HSV) format and using color threshold APP. Blob features are extracted by using (BOF) which used the Speed up Robust Features (SURF) algorithm. Furthermore... the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) algorithms are used for gesture recognition. The Recognized gesture is converted into voice format.