Real-Time Static Hand Gesture Recognition for American Sign Language (ASL) in Complex Background

Abstract

Hand gestures are powerful means of communication among humans and sign language is the most natural and expres-sive way of communication for dump and deaf people. In this work, real-time hand gesture system is proposed. Ex-perimental setup of the system uses fixed position low-cost web camera with 10 mega pixel resolution mounted on the top of monitor of computer which captures snapshot using Red Green Blue [RGB] color space from fixed distance. This work is divided into four stages such as image preprocessing, region extraction, feature extraction, feature matching. First stage converts captured RGB image into binary image using gray threshold method with noise removed using me-dian filter [medfilt2] and Guassian filter, followed by morphological operations. Second stage extracts hand region us-ing blob and crop is applied for getting region of interest and then "Sobel" edge detection is applied on extracted region. Third stage produces feature vector as centroid and area of edge, which will be compared with feature vectors of a training dataset of gestures using Euclidian distance in the fourth stage. Least Euclidian distance gives recognition of perfect matching gesture for display of ASL alphabet, meaningful words using file handling. This paper includes ex-periments for 26 static hand gestures related to A-Z alphabets. Training dataset consists of 100 samples of each ASL symbol in different lightning conditions, different sizes and shapes of hand. This gesture recognition system can reliably recognize single-hand gestures in real time and can achieve a 90.19% recognition rate in complex background with a "minimum-possible constraints" approach.