Real-time American Sign Language Recognition using wrist-worn motion and surface EMG sensors

2015 ◽  
Author(s):  
Jian Wu ◽  
Zhongjun Tian ◽  
Lu Sun ◽  
Leonardo Estevez ◽  
Roozbeh Jafari
Keyword(s):  
American Sign Language ◽  
Real Time ◽  
Sign Language ◽  
Surface Emg ◽  
American Sign ◽  
Language Recognition ◽  
Sign Language Recognition

scholarly journals A Real-Time American Sign Language Recognition System using Convolutional Neural Network for Real Datasets

TEM Journal ◽  
2020 ◽  
pp. 937-943
Author(s):  
Rasha Amer Kadhim ◽  
Muntadher Khamees
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
American Sign Language ◽  
Real Time ◽  
Sign Language ◽  
Recognition System ◽  
High Accuracy ◽  
American Sign ◽  
Language Recognition ◽  
Sign Language Recognition

In this paper, a real-time ASL recognition system was built with a ConvNet algorithm using real colouring images from a PC camera. The model is the first ASL recognition model to categorize a total of 26 letters, including (J & Z), with two new classes for space and delete, which was explored with new datasets. It was built to contain a wide diversity of attributes like different lightings, skin tones, backgrounds, and a wide variety of situations. The experimental results achieved a high accuracy of about 98.53% for the training and 98.84% for the validation. As well, the system displayed a high accuracy for all the datasets when new test data, which had not been used in the training, were introduced.

Real Time ASL (American Sign Language) Recognition

2019 ◽  
Vol 7 (2) ◽  
pp. 848-851
Author(s):  
Fatima Ansari ◽  
Anwar Hussain Mistry ◽  
Yusuf Mirkar ◽  
Alim Merchant
Keyword(s):  
American Sign Language ◽  
Real Time ◽  
Sign Language ◽  
American Sign ◽  
Language Recognition ◽  
Sign Language Recognition

A REAL – TIME SYSTEM FOR RECOGNITION OF AMERICAN SIGN LANGUAGE BY USING DEEP LEARINING

2021 ◽  
Vol 1 (10) ◽  
pp. 316-322
Author(s):  
Mohit Panwar ◽  
Rohit Pandey ◽  
Rohan Singla ◽  
Kavita Saxena
Keyword(s):  
American Sign Language ◽  
Real Time ◽  
Sign Language ◽  
Gesture Recognition ◽  
Recognition System ◽  
American Sign ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Real Time System ◽  
Static Gesture Recognition

Every day we see many people, who are facing illness like deaf, dumb etc. There are not as many technologies which help them to interact with each other. They face difficulty in interacting with others. Sign language is used by deaf and hard hearing people to exchange information between their own community and with other people. Computer recognition of sign language deals from sign gesture acquisition and continues till text/speech generation. Sign gestures can be classified as static and dynamic. However static gesture recognition is simpler than dynamic gesture recognition but both recognition systems are important to the human community. The ASL American sign language recognition steps are described in this survey. There are not as many technologies which help them to interact with each other. They face difficulty in interacting with others. Image classification and machine learning can be used to help computers recognize sign language, which could then be interpreted by other people. Earlier we have Glove-based method in which the person has to wear a hardware glove, while the hand movements are getting captured. It seems a bit uncomfortable for practical use. Here we use visual based method. Convolutional neural networks and mobile ssd model have been employed in this paper to recognize sign language gestures. Preprocessing was performed on the images, which then served as the cleaned input. Tensor flow is used for training of images. A system will be developed which serves as a tool for sign language detection. Tensor flow is used for training of images. Keywords: ASL recognition system, convolutional neural network (CNNs), classification, real time, tensor flow

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