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Sign Language Assistant Using Raspberry Pi

Viraj Hanmante, Aniket Sadaphule, A. M. Deshmukh

Abstract


Sign language communication has been the significant way of communication concerning dumb, deaf and blind people. Therefore, Sign Language Assistant can be used as a bridge between normal people and disabled people to create an invisible path of understanding. The soul of the system is a Raspberry Pi controller which comes with an in-build OS to help it perform operations without any time lag. The Camera is interfaced to a controller to detect the sign image and compare it with the cloud database, further the controller will provide the corresponding result through Bluetooth speaker which is used as one of the outputs for the system. This process will help, as a normal person will be able to understand the thoughts of the disabled person. Similarly, the Microphone is installed with Raspberry Pi through audio jack on the controller which acts as an input source for the system. When a normal person speaks into the microphone, the controller will provide the respective text w.r.t speech with the help of Speech to Text API, which is available in the Raspberry Pi. The output text will be displayed on the LCD screen interfaced with the Raspberry Pi. Hence, the system can be used as an assistant to the disabled people and normal people.


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References


Karmel A, Sharma A, Pandya M, Garg D. IoT based Assistive Device for Deaf, Dumb and Blind People. Procedia Comput Sci. 2019 January 1;165:259–69. doi: 10.1016/j.procs.2020.01.080.

Senthilkumar G, Gopalakrishnan K, Sathish Kumar V. Embedded image capturing system using Raspberry PI system. (IJETTCS). Vol. 3(2); March–April 2014. p. 213–5.

Yi C, Tian Y, Arditi A. Portable camera-based assistive text and product label reading from hand-held objects for blind persons. IEEE/ASME Trans Mechatron. 2013 May 17;19(3):808–17. doi: 10.1109/TMECH.2013.2261083.

Abdallah EE, Fayyoumi E. Assistive technology for deaf people based on android platform. Procedia Comput Sci. 2016 January 1;94:295–301. doi: 10.1016/j.procs.2016.08.044.

Harish N, Poonguzhali S. Design and development of hand gesture recognition system for speech impaired people. In2015 International Conference on Industrial Instrumentation and Control (ICIC). IEEE; 2015 May 28. p. 1129–33.

Ahire PG, Tilekar KB, Jawake TA, Warale PB. Two-way communicator between deaf and dumb people and normal people. In2015 International Conference on Computing Communication Control and Automation. IEEE; 2015 February 26. p. 641–4.

Pray JL, Jordan IK. The deaf community and culture at a crossroads: issues and challenges. J Soc Work Disabil Rehabil. 2010;9(2):168–93. doi: 10.1080/1536710X.2010.493486, PMID 20730674.

Sharma MV, Kumar NV, Masaguppi SC, S. Mn, and D.R. Ambika. Virtual talk for deaf, mute, blind and normal humans. Proceedings of the 2013. 1st Texas Instruments India Educators’ Conference, TIIEC 2013, pp. 316–20, April 2013. Bangalore, India.

Kumari S, Mitra SK. Human action recognition using DFT. In2011 Third National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics 2011 Dec 15. KRN, India: IEEE Publications. p. 239–42.

Mehmood Z, Abbas F, Mahmood T, Javid MA, Rehman A, Nawaz T. Content-based image retrieval based on visual words fusion versus features fusion of local and global features. Arab J Sci Eng. 2018 December 1;43(12):7265–84. doi: 10.1007/s13369–018–3062–0.

Tripathy AK, Jadhav D, Barreto SA, Rasquinha D, Mathew SS. Voice for the mute. In: Proceedings of the 2015 international conference on technologies for sustainable development. International Center for Trade and Sustainable Development; 2015, February. p. 2015.


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