Hand Gesture Recognition for Deaf and Mute
Praveen Kumar1, Tushar Sharma2, Seema Rawat3, Saksham Bhagat4

1Dr. Praveen Kumar*, Student: Associate Professor, Computer Science & Engg, Amity University Uttar Pradesh , Noida, India
2Dr. Seema Rawat, Student: Associate Professor, Information Technology, Amity University Uttar Pradesh, Noida, India
3Tushar Sharma, Student, Computer Science & Engg, Amity University Uttar Pradesh, Noida, India
4Saksham Bhagat, Student, Computer Science & Engg, Amity University Uttar Pradesh , Noida, India

Manuscript received on November 14, 2019. | Revised Manuscript received on 23 November, 2019. | Manuscript published on December 10, 2019. | PP: 1238-1242 | Volume-9 Issue-2, December 2019. | Retrieval Number: B6294129219/2019©BEIESP | DOI: 10.35940/ijitee.B6294.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Communication is the fundamental channel between individuals to speak with each other. In the world of communication gestures and signals is a great deal and a lot of research work has been done in the course of past decades. With the end goal to enhance the recognition rate of systems, numerous specialists have conveyed strategies, for example, HMM, Artificial Neural Networks, and Kinect stage. Effective algorithms for segmentation, classification, pattern matching, and recognition have evolved. Gesture-based communication is generally utilized by people with hearing disabilities to speak with one another helpfully utilizing hand motions. The system uses image processing technology and neural networking for the capturing and conversion of gestures. A number of python packages are used to process and generate the results. The application uses laptop webcam for capturing gestures and recognize gestures shown by the user. The application uses TensorFlow and Keras to generate the model for datasets. The gestures shown by the user are compared with stored gestures and the corresponding output is generated along with speech output. The application thus eliminates the communication barrier between hearing impaired-mute and normal people. 
Keywords: Gestures, Recognition System, Image Processing, Neural Network, TensorFlow.
Scope of the Article: Pattern Recognition and Analysis