Hand Gesture Recognition using Depth Data for Indian Sign Language

Singhal, Shikha and Singhal , Shalakha (2013) Hand Gesture Recognition using Depth Data for Indian Sign Language. BTech thesis.



It is hard for most people who are not familiar with a sign language to communicate without an interpreter. Thus, a system that transcribes symbols in sign languages into plain text can help with real-time communication, and it may also provide interactive training for people to learn a sign language. A sign language uses manual communication and body language to convey meaning. The depth data for five different gestures corresponding to alphabets Y, V, L, S, I was obtained from online database. Each segmented gesture is represented by its timeseries curve and feature vector is extracted from it. To recognise the class of input noisy hand shape, distance metric for hand dissimilarity measure, called Finger-Earth Mover’s Distance (FEMD) is used. As it only matches fingers while not the complete hand shape, it can distinguish hand gestures of slight differences better.

Item Type:Thesis (BTech)
Uncontrolled Keywords:Depth data;Hand gesture;Sign language;Segmentation;Time-Series Curve;Finger Earth Mover’s Distance
Subjects:Engineering and Technology > Electronics and Communication Engineering > Image Processing
Divisions: Engineering and Technology > Department of Electronics and Communication Engineering
ID Code:5033
Deposited By:Hemanta Biswal
Deposited On:05 Dec 2013 16:26
Last Modified:05 Dec 2013 16:26
Supervisor(s):Mahapatra, K K

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