https://orcid.org/0000-0001-9593-5308
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Abstract
Hearing-impaired people can interact with other people through sign language. The proposed system tears down the communication barrier between Hard of hearing (HoH) community and those who do not know their sign language. In this paper, we have developed an algorithm to detect and segment the hand region from a depth image using the Microsoft Kinect sensor. The proposed algorithm works well in the cluttered environment, e.g. skin color background and hand overlaps the face. Convolution Neural networks (CNN) are applied to automatically construct features from Indian sign language (ISL) signs. These features are invariant to rotation and scaling. The proposed system recognizes gestures accurately up to 99.3%.
Additional information
Notes on contributors
Jayesh Gangrade
Jayesh Gangrade received an MTech degree with honours in computer science and engineering from MANIT Bhopal 2008. He has worked as a project engineer in a research center in Wipro Technology Bangalore. He earned his PhD in computer vision from Maulana Azad National Institute of Technology, Bhopal. His research interests are in the areas of computer vision, image processing, and pattern recognition.
Jyoti Bharti
Jyoti Bharti is currently working as an assistant professor in the Department of Computer Science and Engineering, MANIT Bhopal. She earned her PhD in image processing from Maulana Azad National Institute of Technology, Bhopal. She has 15 years of teaching experience; her research interests include image processing, computer vision, and biometrics. She published more than 30 articles in a refereed journal. Email: [email protected]