Analytical Study And Overview On Glove Based Indian Sign Language Interpretation Technique

2015 ◽  
Author(s):  
P. Das ◽  
S. Paul ◽  
B. Neogi ◽  
M. Chowdhury ◽  
R. De
Keyword(s):  
Sign Language ◽  
Analytical Study ◽  
Language Interpretation ◽  
Indian Sign Language

INDIAN SIGN LANGUAGE RECOGNITION SYSTEM USING OPENPOSE

2019 ◽  
Vol 7 (2) ◽  
pp. 43
Author(s):  
MALHOTRA POOJA ◽  
K. MANIAR CHIRAG ◽  
V. SANKPAL NIKHIL ◽  
R. THAKKAR HARDIK ◽  
◽  
...  
Keyword(s):  
Sign Language ◽  
Recognition System ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Indian Sign Language

Indian Sign Language Recognition on PYNQ Board

2020 ◽  
Vol 13 ◽  
Author(s):  
Sukhendra Singh ◽  
G. N. Rathna ◽  
Vivek Singhal
Keyword(s):  
Sign Language ◽  
Hand Gesture ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Hand Gestures ◽  
Depth Images ◽  
Kinect Camera ◽  
Impaired People ◽  
Web Camera ◽  
Indian Sign Language

Introduction: Sign language is the only way to communicate for speech-impaired people. But this sign language is not known to normal people so this is the cause of barrier in communicating. This is the problem faced by speech impaired people. In this paper, we have presented our solution which captured hand gestures with Kinect camera and classified the hand gesture into its correct symbol. Method: We used Kinect camera not the ordinary web camera because the ordinary camera does not capture its 3d orientation or depth of an image from camera however Kinect camera can capture 3d image and this will make classification more accurate. Result: Kinect camera will produce a different image for hand gestures for ‘2’ and ‘V’ and similarly for ‘1’ and ‘I’ however, normal web camera will not be able to distinguish between these two. We used hand gesture for Indian sign language and our dataset had 46339, RGB images and 46339 depth images. 80% of the total images were used for training and the remaining 20% for testing. In total 36 hand gestures were considered to capture alphabets and alphabets from A-Z and 10 for numeric, 26 for digits from 0-9 were considered to capture alphabets and Keywords. Conclusion: Along with real-time implementation, we have also shown the comparison of the performance of the various machine learning models in which we have found out the accuracy of CNN on depth- images has given the most accurate performance than other models. All these resulted were obtained on PYNQ Z2 board.

Design and Development of a Humanoid Robot for Sign Language Interpretation

2021 ◽  
Vol 2 (3) ◽  
Author(s):  
Ragib Amin Nihal ◽  
Nawara Mahmood Broti ◽  
Shamim Ahmed Deowan ◽  
Sejuti Rahman
Keyword(s):  
Sign Language ◽  
Humanoid Robot ◽  
Design And Development ◽  
Language Interpretation
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