Alphabetical Gesture Recognition of American Sign Language using E-Voice Smart Glove

2020 ◽  
Author(s):  
Muhammad Saad Amin ◽  
Muhammad Talha Amin ◽  
Muhammad Yasir Latif ◽  
Ali Asghar Jathol ◽  
Nisar Ahmed ◽  
...  
Keyword(s):  
American Sign Language ◽  
Sign Language ◽  
Gesture Recognition ◽  
American Sign

scholarly journals Web Based Recognition and Translation of American Sign Language with CNN and RNN

2021 ◽  
Vol 17 (01) ◽  
pp. 34
Author(s):  
Dhanashree Shyam Bendarkar ◽  
Pratiksha Appasaheb Somase ◽  
Preety Kalyansingh Rebari ◽  
Renuka Ramkrishna Paturkar ◽  
Arjumand Masood Khan
Keyword(s):  
American Sign Language ◽  
Sign Language ◽  
Gesture Recognition ◽  
Short Term Memory ◽  
Hand Movements ◽  
American Sign ◽  
Static Gesture Recognition ◽  
Dynamic Gestures ◽  
Spatio Temporal ◽  
Dynamic Gesture Recognition

Individuals with hearing hindrance utilize gesture based communication to exchange their thoughts. Generally hand movements are used by them to communicate among themselves. But there are certain limitations when they communicate with other people who cannot understand these hand movements. There is a need to have a mechanism that can act as a translator between these people to communicate. It would be easier for these people to interact if there exists direct infrastructure that is able to convert signs to text and voice messages. As of late, numerous such frameworks for gesture based communication acknowledgment have been developed. But most of them are made either for static gesture recognition or dynamic gesture recognition. As sentences are generated using combinations of static and dynamic gestures, it would be simpler for hearing debilitated individuals if such computerized frameworks can detect both the static and dynamic motions together. We have proposed a design and architecture of American Sign Language (ASL) recognition with convolutional neural networks (CNN). This paper utilizes a pretrained VGG-16 architecture for static gesture recognition and for dynamic gesture recognition, spatiotemporal features were learnt with the complex architecture, called deep learning. It contains a bidirectional convolutional Long Short Term Memory network (ConvLSTM) and 3D convolutional neural network (3DCNN) and this architecture is responsible to extract  2D spatio temporal features.

scholarly journals A Multi-Modular Approach for Gesture Recognition and Text Formulation in American Sign Language

2020 ◽  
Vol 9 (7) ◽  
pp. 217-224
Author(s):  
Sarvesh Joglekar ◽  
Hrishikesh Sawant ◽  
Aayush Jain ◽  
Priya Dhadda ◽  
Pankaj Sonawane
Keyword(s):  
American Sign Language ◽  
Sign Language ◽  
Gesture Recognition ◽  
Modular Approach ◽  
American Sign

scholarly journals Recognition of American Sign Language Gestures in a Virtual Reality Using Leap Motion

2019 ◽  
Vol 9 (3) ◽  
pp. 445 ◽  
Author(s):  
Aurelijus Vaitkevičius ◽  
Mantas Taroza ◽  
Tomas Blažauskas ◽  
Robertas Damaševičius ◽  
Rytis Maskeliūnas ◽  
...  
Keyword(s):  
Virtual Reality ◽  
American Sign Language ◽  
Sign Language ◽  
Gesture Recognition ◽  
Three Dimensional ◽  
Leap Motion ◽  
American Sign ◽  
3D Space

We perform gesture recognition in a Virtual Reality (VR) environment using dataproduced by the Leap Motion device. Leap Motion generates a virtual three-dimensional (3D) handmodel by recognizing and tracking user‘s hands. From this model, the Leap Motion applicationprogramming interface (API) provides hand and finger locations in the 3D space. We present asystem that is capable of learning gestures by using the data from the Leap Motion device and theHidden Markov classification (HMC) algorithm. We have achieved the gesture recognitionaccuracy (mean ± SD) is 86.1 ± 8.2% and gesture typing speed is 3.09 ± 0.53 words per minute(WPM), when recognizing the gestures of the American Sign Language (ASL).

scholarly journals Sign Language Recognition using CNN

2021 ◽  
Vol 9 (9) ◽  
pp. 826-830
Author(s):  
Aniket Wattamwar
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Image Segmentation ◽  
American Sign Language ◽  
Sign Language ◽  
Digital Image Processing ◽  
Gesture Recognition ◽  
Digital Image ◽  
Research Work ◽  
American Sign

Abstract: This research work presents a prototype system that helps to recognize hand gesture to normal people in order to communicate more effectively with the special people. Aforesaid research work focuses on the problem of gesture recognition in real time that sign language used by the community of deaf people. The problem addressed is based on Digital Image Processing using CNN (Convolutional Neural Networks), Skin Detection and Image Segmentation techniques. This system recognizes gestures of ASL (American Sign Language) including the alphabet and a subset of its words. Keywords: gesture recognition, digital image processing, CNN (Convolutional Neural Networks), image segmentation, ASL (American Sign Language), alphabet

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