Pakistan Sign Language Detection using PCA and KNN

Every society has a large majority group of disable people. The technology is developing day by day but no significant developments are undertaken for the improvement of these people. Sign language is an efficient mean of information exchange with special people, such as Deaf and Dumb people, they communicate with each other through sign language, but it become difficult when they communicate to outer world so sign language is used for this purpose. Different research has been done for this in America, Indonesia and India, but not much work done in Pakistan. In this research paper, author introduce a system for recognizing Pakistan Sign Language (PSL) including the alphabet to facilitate communication between special people and normal. This system capture input through webcam without making use of any additional hardware, then using segmentation approach we separate hand from the background and extract required feature from image using Principal Component Analysis (PCA) and then finally classifies the gesture feature by utilizing K Nearest Neighbors (KNN). This research will fill the communication gap between the deaf and normal people of the Pakistan country.

@article{Malik2018,
  title     = {Pakistan Sign Language Detection using PCA and KNN},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {9},
  number    = {4},
  year      = {2018},
  publisher = {The Science and Information Organization},
  author    = {Muhammad Sheraz Arshad Malik and Naila Kousar and Tahir Abdullah and Muhammad Ahmed and Faiqa Rasheed and Muhammad Awais},
  doi       = {10.14569/IJACSA.2018.090414},
  url       = {https://doi.org/10.14569/IJACSA.2018.090414}
}