Artificial neural network based method for Indian sign language recognition

• Published in: 2013 IEEE Conference on Information and Communication Technologies pp. 1080 - 1085
• Main Authors: Adithya, V., Vinod, P. R., Gopalakrishnan, Usha
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.04.2013
• Subjects: Artificial neural network; Assistive technology; Central moments; Distance transform; Feature extraction; Fourier descriptors; Gesture recognition; Hand segmentation; Image color analysis; Indian sign language; Projections; Shape; Transforms; Vectors
• ISBN: 9781467357593; 1467357596
• DOI: 10.1109/CICT.2013.6558259

Sign Language is a language which uses hand gestures, facial expressions and body movements for communication. A sign language consists of either word level signs or fingerspelling. It is the only communication mean for the deaf-dumb community. But the hearing people never try to learn the sign language. So the deaf people cannot interact with the normal people without a sign language interpreter. This causes the isolation of deaf people in the society. So a system that automatically recognizes the sign language is necessary. The implementation of such a system provides a platform for the interaction of hearing disabled people with the rest of the world without an interpreter. In this paper, we propose a method for the automatic recognition of fingerspelling in Indian sign language. The proposed method uses digital image processing techniques and artificial neural network for recognizing different signs.