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.

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

scholarly journals American Sign Language Recognition Using Leap Motion Controller with Machine Learning Approach

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3554 ◽  
Author(s):  
Teak-Wei Chong ◽  
Boon-Giin Lee
Keyword(s):  
American Sign Language ◽  
Sign Language ◽  
Recognition System ◽  
Support Vector ◽  
Leap Motion ◽  
American Sign ◽  
Language Recognition ◽  
Motion Controller ◽  
Sign Language Recognition ◽  
Leap Motion Controller

Sign language is intentionally designed to allow deaf and dumb communities to convey messages and to connect with society. Unfortunately, learning and practicing sign language is not common among society; hence, this study developed a sign language recognition prototype using the Leap Motion Controller (LMC). Many existing studies have proposed methods for incomplete sign language recognition, whereas this study aimed for full American Sign Language (ASL) recognition, which consists of 26 letters and 10 digits. Most of the ASL letters are static (no movement), but certain ASL letters are dynamic (they require certain movements). Thus, this study also aimed to extract features from finger and hand motions to differentiate between the static and dynamic gestures. The experimental results revealed that the sign language recognition rates for the 26 letters using a support vector machine (SVM) and a deep neural network (DNN) are 80.30% and 93.81%, respectively. Meanwhile, the recognition rates for a combination of 26 letters and 10 digits are slightly lower, approximately 72.79% for the SVM and 88.79% for the DNN. As a result, the sign language recognition system has great potential for reducing the gap between deaf and dumb communities and others. The proposed prototype could also serve as an interpreter for the deaf and dumb in everyday life in service sectors, such as at the bank or post office.

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