Author(s)

Mathavan Suresh Anand^1,2, Nagarajan Mohan Kumar³, Angappan Kumaresan³

¹Anna University, Chennai, India.
²Department of Computer Science and Engineering, Sri Sai Ram Engineering College, Anna University, Chennai, India.
³Department of Electronics and Communication Engineering, SKP Engineering College, Anna University, Tirvannamalai, India.

Hand gesture recognition system is considered as a way for more intuitive and proficient human computer interaction tool. The range of applications includes virtual prototyping, sign language analysis and medical training. In this paper, an efficient Indian Sign Language Recognition System (ISLR) is proposed for deaf and dump people using hand gesture images. The proposed ISLR system is considered as a pattern recognition technique that has two important modules: feature extraction and classification. The joint use of Discrete Wavelet Transform (DWT) based feature extraction and nearest neighbour classifier is used to recognize the sign language. The experimental results show that the proposed hand gesture recognition system achieves maximum 99.23% classification accuracy while using cosine distance classifier.

Hand Gesture, Sign Language Recognition, Thresholding, Wavelet Transform, Nearest Neighbour Classifier

Anand, M. , Kumar, N. and Kumaresan, A. (2016) An Efficient Framework for Indian Sign Language Recognition Using Wavelet Transform. Circuits and Systems, 7, 1874-1883. doi: 10.4236/cs.2016.78162.