Nearest neighbour classification of Indian sign language gestures using kinect camera

• Published in: Sadhana (Bangalore) Vol. 41; no. 2; pp. 161 - 182
• Main Authors: ANSARI, ZAFAR AHMED, HARIT, GAURAV
• Format: Journal Article
• Language: English
• Published: New Delhi Springer India 01.02.2016
• Subjects: Engineering; Indian sign language recognition; multi-class classification; gesture recognition
• ISSN: 0256-2499; 0973-7677
• DOI: 10.1007/s12046-015-0405-3

People with speech disabilities communicate in sign language and therefore have trouble in mingling with the able-bodied. There is a need for an interpretation system which could act as a bridge between them and those who do not know their sign language. A functional unobtrusive Indian sign language recognition system was implemented and tested on real world data. A vocabulary of 140 symbols was collected using 18 subjects, totalling 5041 images. The vocabulary consisted mostly of two-handed signs which were drawn from a wide repertoire of words of technical and daily-use origins. The system was implemented using Microsoft Kinect which enables surrounding light conditions and object colour to have negligible effect on the efficiency of the system. The system proposes a method for a novel, low-cost and easy-to-use application, for Indian Sign Language recognition, using the Microsoft Kinect camera. In the fingerspelling category of our dataset, we achieved above 90% recognition rates for 13 signs and 100% recognition for 3 signs with overall 16 distinct alphabets (A, B, D, E, F, G, H, K, P, R, T, U, W, X, Y, Z) recognised with an average accuracy rate of 90.68%.