Hand Anatomy and Neural Network-Based Recognition for Sign Language

Abstract

This paper presents a model based on hand anatomy and neural network for the recognition of sign language. Feature extraction is done by using FAST and SIFT techniques. Out of these extracted features, only essential hand landmarks are selected using hand anatomy. NN is then used for the training and testing of the model. The proposed model is evaluated on sign language gestures used for medical purposes, general purposes, and family & relative purposes. The results prove that the proposed model has achieved fast and highly accurate results when compared with other available models. The model has achieved a recognizable accuracy of 99.85%, 97.55%, and 98.85%, on medical purposes, general purposes, and family & relative purposes, respectively.

KEYWORDS:

• FAST
• Hand anatomy
• Human-computer interaction
• Neural network
• SIFT
• Sign language

Disclosure statement

No potential conflict of interest was reported by the author(s ).

Additional information

Notes on contributors

Akansha Tyagi

Akansha Tyagi is a PhD research scholar at Maharishi Markandeswar University Mullana, Haryana, India. She received the MTech degree from Maharishi Dayanand University Rohtak (Haryana) and BTech degree from Punjab Technical University (Punjab). Her areas of interest are soft computing, sign language recognition, and computer vision.

Sandhya Bansal

Sandhya Bansal is an associate professor at Maharishi Markandeswar University Mullana, Haryana, India. She received the PhD degree from the same University and BTech degree from Kurukshetra University (Haryana). She supervised 8 MTech candidates. Her areas of interest are WSN, metaheuristics and VRP. She has about 25 research papers in international journals (SCI,Web of Science, Scopus, IGI and Elsevier, etc.). Currently, she is supervising 2 PhD research scholars. Email: [email protected]