Recognition of Indian Sign Languages

Sahara Shetty; Yashaswi G. P

doi:10.21276/ijircst.2021.9.4.9

Abstract

The focal point of this project is about developing a user interface to aid people who have hearing and speaking disabilities. This application would help in recognizing the different signs with respect to the single user. The real-time image is captured using a webcam and dynamically stored. The elements are initialized and saved in KN Neighbor classifiers. These classifiers are used to load the KN Neighbor model. This model predicts the signs dynamically and is built using machine learning techniques. Mobile Net is used as a machine learning package. This model was implemented in three phases. The first phase deals with the user interface where user images are captured using a webcam. In the second phase, the initialized elements are used by the KN Neighbor classifier and then stored as the KN Neighbor model. The third phase involves the prediction of signs.

Keywords

Indian sign language, Tensor flow, squeeze net

References

Abhishek Jain, Lakshita Jain, Ishaan Sharma, Abhishek Chauhan, TECH, Department Of ECE, SRMIST,“ Image Processing Based Speaking System For Mute People Using Hand Gestures”, International Journal Of Engineering Sciences & Research Technology, 2018.
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Cites this article as

S. Shetty, Y. G. P, "Recognition of Indian Sign Languages", International Journal of Innovative Research in Engineering & Management (ijircst), Vol-9, Issue-4, Page No-36-38, 2021. Available from: https://doi.org/10.21276/ijircst.2021.9.4.9

Corresponding Author

Sahara Shetty

Student, Department of Computer Science and Engineering, Srinivas Institute of Technology, Mangalore, India (saharashetty1999@gmail.com)

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