Feature based Algorithmic Analysis on American Sign Language Dataset

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.

@article{Butt2019,
  title     = {Feature based Algorithmic Analysis on American Sign Language Dataset},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {10},
  number    = {5},
  year      = {2019},
  publisher = {The Science and Information Organization},
  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},
  doi       = {10.14569/IJACSA.2019.0100575},
  url       = {https://doi.org/10.14569/IJACSA.2019.0100575}
}