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General Information
    • ISSN: 1793-8236 (Online)
    • Abbreviated Title Int. J. Eng. Technol.
    • Frequency:  Quarterly 
    • DOI: 10.7763/IJET
    • Executive Editor: Ms.Yoyo Y. Zhou
    • Abstracting/ Indexing: Chemical Abstracts Services (CAS) EBSCO, Google Scholar, Ulrich Periodicals Directory, Crossref, ProQuest, Index CopernicusINSPEC.
    • E-mail: ijet@vip.163.com
Prof. T. Hikmet Karakoc
Anadolu University, Faculty of Aeronautics and Astronautics, Turkey

IJET 2012 Vol.4(5): 537-542 ISSN: 1793-8244
DOI: 10.7763/IJET.2012.V4.427

A Video Based Indian Sign Language Recognition System (INSLR) Using Wavelet Transform and Fuzzy Logic

P. V. V. Kishore and P. Rajesh Kumar
Abstract—This paper proposes a complete skeleton of isolated Video Based Indian Sign Language Recognition System (INSLR) that integrates various image processing techniques and computational intelligence techniques in order to deal with sentence recognition. The system is developed to improve communication between hearing impaired people and normal people promising them better social prospects. A wavelet based video segmentation technique is proposed which detects shapes of various hand signs and head movement in video based setup. Shape features of hand gestures are extracted using elliptical Fourier descriptions which to the highest degree reduces the feature vectors for an image. Principle component analysis (PCA) still minimizes the feature vector for a particular gesture video and the features are not affected by scaling or rotation of gestures within a video which makes the system more flexible. Features generated using these techniques makes the feature vector unique for a particular gesture. Recognition of gestures from the extracted features is done using a Sugeno type fuzzy inference system which uses linear output membership functions. Finally the INSLR system employs an audio system to play the recognized gestures along with text output. The system is tested using a data set of 80 words and sentences by 10 different signers. The experimental results show that our system has a recognition rate of 96%.

Index Terms—Indian sign language, fuzzy inference system, wavelet transform, canny edge operator, image fusion, elliptical fourier descriptors, principle component analysis.

P. V. V. Kishore is with the Andhra University College of Engineering, Visakhapatnam, India.-530017 (Tel: 9866535444, e-mail: pvvkishore@gmail.com).
P. Rajesh Kumar is with the Department of Electronics and Communication Engineering, Andhra University college of engineering, Visakhapatnam, Andhra Pradesh, India, 530017 (e-mail: rajeshauce@gmail.com).


Cite: P. V. V. Kishore and P. Rajesh Kumar, "A Video Based Indian Sign Language Recognition System (INSLR) Using Wavelet Transform and Fuzzy Logic," International Journal of Engineering and Technology vol. 4, no. 5, pp.537-542, 2012.
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