Automatic Indian Sign Language recognition system

Author

Dixit, K. ; Jalal, Anand Singh

Author_Institution

Dept. of Comput. Eng. & Applic., GLA Univ., Mathura, India

fYear

2013

fDate

22-23 Feb. 2013

Firstpage

883

Lastpage

887

Abstract

Sign Language is the most natural and expressive way for the hearing impaired. This paper presents a methodology which recognizes the Indian Sign Language (ISL) and translates into a normal text. The methodology consists of three stages, namely a training phase, a testing phase and a recognition phase. Combinational parameters of Hu invariant moment and structural shape descriptors are created to form a new feature vector to recognize sign. A multi-class Support Vector Machine (MSVM) is used for training and recognizing signs of ISL. The effectiveness of the proposed method is validated on a dataset having 720 images. Experimental results demonstrate that the proposed system can successfully recognize hand gesture with 96% recognition rate.

Keywords

learning (artificial intelligence); sign language recognition; support vector machines; Hu invariant moment parameter; ISL; Indian sign language recognition system; MSVM; feature vector; hand gesture recognition; multiclass support vector machine; recognition phase; sign training; structural shape descriptor; testing phase; training phase; Assistive technology; Feature extraction; Gesture recognition; Image segmentation; Shape; Testing; Training; Indian Sign Language (ISL); Multi-class Support Vector Machine (MSVM);

fLanguage

English

Publisher

ieee

Conference_Titel

Advance Computing Conference (IACC), 2013 IEEE 3rd International

Conference_Location

Ghaziabad

Print_ISBN

978-1-4673-4527-9

Type

conf

DOI

10.1109/IAdCC.2013.6514343

Filename

6514343