American Sign Language Recognition using Deep Learning and Computer Vision

2018 ◽  
Author(s):  
Kshitij Bantupalli ◽  
Ying Xie
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
American Sign Language ◽  
Sign Language ◽  
American Sign ◽  
Language Recognition ◽  
Sign Language Recognition

scholarly journals American Sign Language Recognition Using an FMCW Wireless Sensor (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13781-13782
Author(s):  
Yuanqi Du ◽  
Nguyen Dang ◽  
Riley Wilkerson ◽  
Parth Pathak ◽  
Huzefa Rangwala ◽  
...  
Keyword(s):  
American Sign Language ◽  
Sign Language ◽  
Continuous Wave ◽  
The Body ◽  
Wireless Sensor ◽  
American Sign ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Wave Radar ◽  
Cloud Points

In today's digital world, rapid technological advancements continue to lessen the burden of tasks for individuals. Among these tasks is communication across perceived language barriers. Indeed, increased attention has been drawn to American Sign Language (ASL) recognition in recent years. Camera-based and motion detection-based methods have been researched extensively; however, there remains a divide in communication between ASL users and non-users. Therefore, this research team proposes the use of a novel wireless sensor (Frequency-Modulated Continuous-Wave Radar) to help bridge the gap in communication. In short, this device sends out signals that detect the user's body positioning in space. These signals then reflect off the body and back to the sensor, developing thousands of cloud points per second, indicating where the body is positioned in space. These cloud points can then be examined for movement over multiple consecutive time frames using a cell division algorithm, ultimately showing how the body moves through space as it completes a single gesture or sentence. At the end of the project, 95% accuracy was achieved in one-object prediction as well as 80% accuracy on cross-object prediction with 30% other objects' data introduced on 19 commonly used gestures. There are 30 samples for each gesture per person from three persons.

Sign Language Recognition Using Deep Learning and Computer Vision

2020 ◽  
Vol 12 (05-SPECIAL ISSUE) ◽  
pp. 964-968
Author(s):  
Sabeenian R.S ◽  
S. Sai Bharathwaj ◽  
M. Mohamed Aadhil
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Sign Language ◽  
Language Recognition ◽  
Sign Language Recognition
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