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Indian Sign Language Character Recognition using Neural Networks

Publication Type : Journal Article

Publisher : IJCA Special Issue on Recent Trends in Pattern Recognition and Image Analysis

Source : IJCA Special Issue on Recent Trends in Pattern Recognition and Image Analysis, Volume RTPRIA, Number 1, p.40-45 (2013)

Campus : Coimbatore

School : School of Engineering

Center : Center for Computational Engineering and Networking

Department : Computer Science

Verified : Yes

Year : 2013

Abstract : Deaf and dumb people communicate among themselves using sign languages, but they find it difficult to expose themselves to the outside world. This paper proposes a method to convert the Indian Sign Language (ISL) hand gestures into appropriate text message. In this paper the hand gestures corresponding to ISL English alphabets are captured through a webcam. In the captured frames the hand is segmented and the neural network is used to recognize the alphabet. The features such as angle made between fingers, number of fingers that are fully opened, fully closed or semi closed and identification of each finger are used as input to the neural network. Experimentation done for single hand alphabets and the results are summarized.

Cite this Research Publication : Dr. Padmavathi S., S, S. M., and V, V., “Indian Sign Language Character Recognition using Neural Networks”, IJCA Special Issue on Recent Trends in Pattern Recognition and Image Analysis, vol. RTPRIA, pp. 40-45, 2013.

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