International Research journal of Management Science and Technology

  ISSN 2250 - 1959 (online) ISSN 2348 - 9367 (Print) New DOI : 10.32804/IRJMST

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INTERACTIVE SIGN LANGUAGE SYSTEM FOR DEAF AND MUTE PEOPLE USING XBOX360

    4 Author(s):  CHAITANYA SAWLE,DEV S. DESAI,AADITYA S. NANGARE,RUTUJA J. SHEWALE

Vol -  10, Issue- 3 ,         Page(s) : 100 - 103  (2019 ) DOI : https://doi.org/10.32804/IRJMST

Abstract

The proposed system is being developed for deaf and mute people who uses sign language. Sign language is visual way of communication using hand gesture, body language and facial expressions. Hand gesture is one of the most natural and expressive ways for hearing impaired. It has primarily developed for culturally deaf people however, because of the complexity of dynamic gestures either static gestures, postures, or a small set of dynamic gestures are focused by most researchers. Here, kinect motion sensor device is used to recognize the gesture of user. As real-time recognition of a large set of dynamic gestures is considered, some efficient algorithms and models are needed. Framework is being proposed which can capture gesture as input and translate it with help of database and programs. Here an efficient algorithm is used to recognize the gesture and translate them. In order to recognize the gesture in both training and translation mode a grid view algorithm has been used.

[1] D. M. Capilla, “On Sign Language Translator Using Microsoft Kinect XBOX 360,” M. Sc.             Thesis, University of Tennessee, Knoxville, USA.
[2] A. Farhadi, D.  Forsyth, “Aligning ASL for statistical translation using a discriminative word model,” University of Illinois. 
[3] S. Young, “ HTK: Hidden Markov Model Toolkit VI.5,” Cambridge Univ. Eng. Dept., Speech Group and Entropie Research Lab. Inc., Washington DC.
[4] P. Mekala, Y. Gao, J. Fan and A. Davari, "Real-time sign language recognition based on neural network architecture," in Proc. 2011 IEEE 43rd Southeastern Symposium        on System Theory, Auburn, AL, 2011, pp. 195-199. 
[5] Y.F. Admasu, and K. Raimond, “Ethiopian Sign Language Recognition Using Artificial Neural Network,” in proc. 10th International Conference on Intelligent Systems        Design and Applications, 2010. 9951000. 
[6] J. Atwood, M. Eicholtz, and J. Farrell, “American Sign Language Recognition System. Artificial Intelligence and Machine Learning for Engineering Design,” Dept. of Mechanical Engineering, Carnegie Mellon University, 2012

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