Recognition of Sign Language Based on Hand Gestures
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Keywords

KNN, Edge detection, Neural network, Grey conversion, Sign language recognition.

How to Cite

Gunji, B. M., Bhargav, N. . M., Dey, A. ., Zeeshan Mohammed, . I. K. ., & Sathyajith, S. . (2021). Recognition of Sign Language Based on Hand Gestures. Journal of Advances in Applied &Amp; Computational Mathematics, 8, 21–32. https://doi.org/10.15377/2409-5761.2021.08.3
Received 2021-06-17
Accepted 2021-08-11
Published 2021-11-02

Abstract

The target of SLR or sign language recognition is to interpret the sign language into text, respectively. So the deaf and mute people can communicate with ordinary people easily. Sign language recognition has a tremendous social impact; however, it is challenging due to the significant variations and complexity in the hand actions. There are many existing methods for recognizing sign language that uses handcraft features for describing the motion of sign language and then, based on the features it makes the classification models. To approach the problem, we have discussed considering the KNN that can conveniently extract the features. The proposed model can be validated on a real data set.

https://doi.org/10.15377/2409-5761.2021.08.3
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Copyright (c) 2021 Bala Murali Gunji, Nikhil M. Bhargav, Amrita Dey, Isahak Karajagi Zeeshan Mohammed, Sachdev Sathyajith