American Sign Language Alphabet Recognition Using a Neuromorphic Sensor and an Artificial Neural Network

Communication is essential in conveying information from one individual to another. However, not all individuals in the world can communicate verbally. According to WHO, deafness is a hearing loss that affects 466 million people globally, and 34 million are children. So it is necessary to have a non-verbal language learning method for someone who has hearing problems. The purpose of this study is to build a system that can identify non-verbal language so that it can be easily understood in real-time. A high success rate in the system needs a proper method to be applied in the system, such as machine learning supported by wavelet feature extraction and different classification methods in image processing. Machine learning was applied in the system because of its ability to recognize and compare the classification results in four different methods. The four classifications used to compare the hand gesture recognition from American Sign Language are the Multi-Class SVM classification, Backpropagation Neural Network Backpropagation, K - Nearest Neighbor (K-NN), and Naïve Bayes. The simulation test of the four classification methods that have been carried out obtained success rates of 99.3%, 98.28%, 97.7%, and 95.98%, respectively. So it can be concluded that the classification method using the Multi-Class SVM has the highest success rate in the introduction of American Sign Language, which reaches 99.3%. The whole system is designed and tested using MATLAB as supporting software and data processing.

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A new framework for sign language alphabet hand posture recognition using geometrical features through artificial neural network (part 1)

Neural Computing and Applications ◽

10.1007/s00521-020-05279-7 ◽

2020 ◽

Author(s):

Hoshang Kolivand ◽

Saba Joudaki ◽

Mohd Shahrizal Sunar ◽

David Tully

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Sign Language ◽

Hand Posture ◽

Hand Posture Recognition ◽

Geometrical Features ◽

Posture Recognition ◽

Artificial Neural ◽

New Framework

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Convolutional Neural Network Based American Sign Language Static Hand Gesture Recognition

International Journal of Ambient Computing and Intelligence ◽

10.4018/ijaci.2019070104 ◽

2019 ◽

Vol 10 (3) ◽

pp. 60-73 ◽

Cited By ~ 2

Author(s):

Ravinder Ahuja ◽

Daksh Jain ◽

Deepanshu Sachdeva ◽

Archit Garg ◽

Chirag Rajput

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

American Sign Language ◽

Sign Language ◽

Automated System ◽

World Health ◽

American Sign ◽

Hand Gestures ◽

The World ◽

Health Organization

Communicating through hand gestures with each other is simply called the language of signs. It is an acceptable language for communication among deaf and dumb people in this society. The society of the deaf and dumb admits a lot of obstacles in day to day life in communicating with their acquaintances. The most recent study done by the World Health Organization reports that very large section (around 360 million folks) present in the world have hearing loss, i.e. 5.3% of the earth's total population. This gives us a need for the invention of an automated system which converts hand gestures into meaningful words and sentences. The Convolutional Neural Network (CNN) is used on 24 hand signals of American Sign Language in order to enhance the ease of communication. OpenCV was used in order to follow up on further execution techniques like image preprocessing. The results demonstrated that CNN has an accuracy of 99.7% utilizing the database found on kaggle.com.

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