Linguistic Properties Based on American Sign Language Recognition with Artificial Neural Networks Using a Sensory Glove and Motion Tracker

2005 ◽  
pp. 1197-1205 ◽  
Author(s):  
Cemil Oz ◽  
Ming C. Leu
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
American Sign Language ◽  
Sign Language ◽  
American Sign ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Artificial Neural

Mexican sign language recognition using normalized moments and artificial neural networks

2014 ◽  
Author(s):  
J-Francisco Solís-V. ◽  
Carina Toxqui-Quitl ◽  
David Martínez-Martínez ◽  
Margarita H.-G.
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Sign Language ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Artificial Neural

scholarly journals Sign Language Recognition using Hybrid Neural Networks

2019 ◽  
Vol 9 (2) ◽  
pp. 1092-1098
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Artificial Neural Networks ◽  
Sign Language ◽  
Back Propagation ◽  
Optimization Techniques ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Sign Languages ◽  
Artificial Neural

Language has a prime role in communication between persons, in learning, in distribution of concepts and in preserving public contacts. The hearing-impaired have to challenge communication obstacles in a mostly hearing-capable culture. There are hundreds Sign Languages that are used all around the world today .The Sign Languages are established depending on the country and area of the deaf public. The aim of sign language recognition is to offer an effectual and correct tool to transcribe hand gesture into text. It can play a vital role in the communiqué between deaf and hearing society. Sign language recognition (SLR), as one of the significant research fields of human–computer interaction (HCI), has produced more and more interest in HCI society. Since, artificial neural networks are best suited for automated pattern recognition problems; they are used as a classification tool for this research. Back propagation is the most important algorithm for training neural networks. But, it easily gets trapped in local minima leading to inaccurate solutions. Therefore, some global search and optimization techniques were required to hybridize with artificial neural networks. One such technique is Genetic algorithms that imitate the principle of natural evolution. So, in this article, a hybrid intelligent system is proposed for sign language recognition in which artificial neural networks are merged with genetic algorithms. Results show that proposed hybrid model outperformed the existing back propagation based system.

REAL-TIME AMERICAN SIGN LANGUAGE RECOGNITION WITH NEURAL NETWORKS

2021 ◽  
Vol 1 (10) ◽  
pp. 263-272
Author(s):  
Mohd Arifullah ◽  
Fais Khan ◽  
Yash Handa
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
American Sign Language ◽  
Sign Language ◽  
Image Recognition ◽  
Time Signal ◽  
American Sign ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Information Sets

Actual-time signal language translator is a crucial milestone in facilitating communication among the deaf community and the general public. Introducing the development and use of yanked sign Language Spelling Translator (ASL) based on the convolutional neural network. We use the pre-skilled Google Net architecture educated inside the ILSVRC2012 database, in addition to the ASL database for Surrey University and Massey university ASL to apply gaining knowledge of switch in this task. We have developed a sturdy version that constantly separates the letters a-e from the original users and any other that separates the spaced characters in maximum cases. Given the limitations of the information sets and the encouraging consequences acquired, we are assured that with similarly studies and further facts, we can produce a totally customized translator for all ASL characters. Keywords: Sign Language, Image Recognition, American Sign Language, Expressions signals, CNN

scholarly journals Optimization of Convolutional Neural Networks Architectures Using PSO for Sign Language Recognition

Axioms ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 139
Author(s):  
Jonathan Fregoso ◽  
Claudia I. Gonzalez ◽  
Gabriela E. Martinez
Keyword(s):  
Neural Networks ◽  
Objective Function ◽  
American Sign Language ◽  
Sign Language ◽  
Convolutional Neural Networks ◽  
Network Performance ◽  
Recognition Rate ◽  
American Sign ◽  
Language Recognition ◽  
Sign Language Recognition

This paper presents an approach to design convolutional neural network architectures, using the particle swarm optimization algorithm. The adjustment of the hyper-parameters and finding the optimal network architecture of convolutional neural networks represents an important challenge. Network performance and achieving efficient learning models for a particular problem depends on setting hyper-parameter values and this implies exploring a huge and complex search space. The use of heuristic-based searches supports these types of problems; therefore, the main contribution of this research work is to apply the PSO algorithm to find the optimal parameters of the convolutional neural networks which include the number of convolutional layers, the filter size used in the convolutional process, the number of convolutional filters, and the batch size. This work describes two optimization approaches; the first, the parameters obtained by PSO are kept under the same conditions in each convolutional layer, and the objective function evaluated by PSO is given by the classification rate; in the second, the PSO generates different parameters per layer, and the objective function is composed of the recognition rate in conjunction with the Akaike information criterion, the latter helps to find the best network performance but with the minimum parameters. The optimized architectures are implemented in three study cases of sign language databases, in which are included the Mexican Sign Language alphabet, the American Sign Language MNIST, and the American Sign Language alphabet. According to the results, the proposed methodologies achieved favorable results with a recognition rate higher than 99%, showing competitive results compared to other state-of-the-art approaches.

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