Final Report (part-3).docx

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HAND GESTURE RECOGNITION SYSTEM FOR DUMB PEOPLE

REFERENCES [1]

Amarjot, S., Devinder, K., Phani, S., Srikrishna, K., and Niraj, A. “An intelligent multi-gesture spotting robot to assist persons with disabilities”, International journal of computer theory and engineering, Vol. 4, No. 6, pp. 1290-1420,2012.

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Alois, F., Stefan, R., Clemens, H., and Martin, R. “Orientation sensing for gesturebased interaction with smart artefacts”, IEEE Transactions on audio, speech, and language processing, Vol.28, No. 8, pp. 1434-1520, 2007.

[3]

Donglin, W., Ajeesh, P., Hye, K., and Hosub, P. “A Deconvolutive neural network for speech classification with applications to home service robot,” IEEE Transactions on instrumentation and measurement, Vol. 59, No. 12, pp. 2334-4620,2010.

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Istvan, I., Zoran, M., and Nikola, D. “Hands-free voice communica- tion with TV”, IEEE Transactions on consumer electronics, Vol. 17, No. 2, pp. 1378-1695, 2011.

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Ibrahim, P., and Srinivasa, R. “Automated speech recognition ap- proach to continuous cue symbols generation”, International journal of power control signal and computation, Vol. 18, No. 8,pp. 434-520,2007.

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Jean, C., and Peter, B., “Recognition of Arm Gestures Using Multiple Orientation Sensors: Gesture Classification”, IEEE Intelligent transpor- tation systems conference on electronics, Vol. 13, No. 1, pp. 334-520,2004.

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Joyeeta, S., and Karen, D. “Indian Sign Language Recognition Using Eigen Value Weighted Euclidean Distance Based Classification Tech- nique”, International journal of advanced computer science applications, Vol. 4, No. 2, pp. 434-820, 2013.

[8]

Ravikiran, J., Kavi, M., Suhas, M., Sudheender, and S., Nitin, V. “Fin- ger Detection for Sign Language Recognition”, Proceedings of the In- ternational multi conference of engineers and computer scientists, Vol. 2, No. 1, pp.

[9] Mark, H., Rebecca, P., Peter, O. “A voice-input voice-output commu- nication aid for people with severe speech impairment”, IEEE Transactions on neural systems and rehabilitation engineering, Vol. 21, No. 1, pp. 1534-4320,2013.

1316110024 , 1316110031 , 1316110045 , 1316110052

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HAND GESTURE RECOGNITION SYSTEM FOR DUMB PEOPLE

[10] 12. Oyaaran, B. “Vision based sign language recognition modelling and recognizing signs with manual and non-manual components”, Inter- national journal of computer science and engineering, Vol. 12, No. Paper- back, 03 pp. 378-1695, 2008. [11] 13. Otiniano, R., and Amara, C. “Finger spelling recognition from rgb-d information using kernel descriptor”, IEEE Transactions on neural sys- tems and rehabilitation engineering, Vol.28, No. 8, pp. 124-184, 2006.

[12] 14. Ravikiran, J., Kavi, M., Suhas, M., Dheeraj, R., Sudheender, S., and Nitin V. “Finger detection for sign language recognition”, Proceedings of the international multi conference of engineers and computer scientists, Vol. 39, No. 12, pp. 334-620, 2009.

[13]

15. Zhengmao, Z., Prashan, R., Monaragala, N., Malin, P. “Dynamic hand gesture recognition system using moment invariants”, IEEE Transactions on neural networking and computing, Vol. 21, No. 1, pp. 1034-1320, 2010.

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HAND GESTURE RECOGNITION SYSTEM FOR DUMB PEOPLE

1316110024 , 1316110031 , 1316110045 , 1316110052

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