Sign Language Recognition System Using Deep Neural Network

2019 ◽  
Author(s):  
Surejya Suresh ◽  
Haridas T.P. Mithun ◽  
M.H. Supriya
Keyword(s):  
Neural Network ◽  
Sign Language ◽  
Deep Neural Network ◽  
Recognition System ◽  
Language Recognition ◽  
Sign Language Recognition

scholarly journals Sign Language Recognition System

2022 ◽  
pp. 1-3
Author(s):  
Dr. Pooja M R ◽  
◽  
Meghana M ◽  
Harshith Bhaskar ◽  
Anusha Hulatti ◽  
...  
Keyword(s):  
Neural Network ◽  
Sign Language ◽  
Recognition System ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Voice Input ◽  
Normal People ◽  
Web Camera ◽  
A New Technique ◽  
Artificial Neural Network Ann

We witness many people who face disabilities like being deaf, dumb, blind etc. They face a lot of challenges and difficulties trying to interact and communicate with others. This paper presents a new technique by providing a virtual solution without making use of any sensors. Histogram Oriented Gradient (HOG) along with Artificial Neural Network (ANN) have been implemented. The user makes use of web camera, which takes input from the user and processes the image of different gestures. The algorithm recognizes the image and identifies the pending voice input. This paper explains two way means of communication between impaired and normal people which implies that the proposed ideology can convert sign language to text and voice.

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.

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