Abstract: Physical disability is one of the factor in human beings, which cannot be ignored. A person who can’t listen by nature is called deaf person. For the representation of their knowledge, a special language is adopted called ‘Sign-Language’. American Sign Language (ASL) is one of the most popular sign language that is used for learning process in deaf persons. For the representation of their knowledge by deaf persons, a special language is adopted ‘Sign-Language’. American Sign Language contains a set of digital images of hands in different shapes or hand gestures. In this paper, we present feature based algorithmic analysis to prepare a significant model for recognition of hand gestures of American Sign Language. To make a machine intelligent, this model can be used to learn efficiently. For effective machine learning, we generate a list of useful features from digital images of hand gestures. For feature extraction, we use Matlab 2018a. For training and testing, we use weka-3-9-3 and Rapid Miner 9 1.0. Both application tools are used to build an effective data modeling. Rapid Miner outperforms with 99.9% accuracy in auto model.

Umair Muneer Butt, Basharat Husnain, Usman Ahmed, Arslan Tariq, Iqra Tariq, Muhammad Aadil Butt and Dr. Muhammad Sultan Zia, “Feature based Algorithmic Analysis on American Sign Language Dataset” International Journal of Advanced Computer Science and Applications(IJACSA), 10(5), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100575

@article{Butt2019,
title = {Feature based Algorithmic Analysis on American Sign Language Dataset},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100575},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100575},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {5},
author = {Umair Muneer Butt and Basharat Husnain and Usman Ahmed and Arslan Tariq and Iqra Tariq and Muhammad Aadil Butt and Dr. Muhammad Sultan Zia}
}