Deep Eigen Space Based ASL Recognition System

Abstract

Sign language is the art of communicating using hand gestures. The communication between an impaired person and the one who doesn't understand sign language could be easy using the computer as a tool. Inconsistent previous works in the field posed a need for more stable and effective model. We take an efficient step in order to tackle the problem of automatic fingerspelling recognition system using the concept that in order to efficiently perceive things, human brain alters some of the information. Moreover, the comparison between previous works and our model is mentioned systematically in this paper. Also, the model is capable to classify the real-time sign without dependency on environment.

Keywords:

• Deep learning
• Event summarization
• Multi-view video
• FASTA
• Local alignment
• Nucleotide sequence

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Shikhar Sharma

Shikhar Sharma obtained his BTech in computer science and engineering from National Institute of Technology, Uttarakhand, India. His current research areas are: computer vision, image processing, object detection, pattern recognition, machine learning and deep learning.*

Krishan Kumar

Krishan Kumar is working as an assistant professor in National Institute of Technology, Uttarakhand, India. He obtained MTech and PhD in computer science and engineering from Visvesvaraya National Institute of Technology, Nagpur, India in 2014 and 2019, respectively. His current research areas are cloud computing, computer vision, deep learning, image processing, object detection, pattern recognition, video analysis, and summarization. E-mail: [email protected]

Navjot Singh

Navjot Singh is working as an assistant professor in Motilal Nehru National Institute of Technology, Allahabad, India. He obtained MTech (computer science and technology) and PhD (computer science) from Jawaharlal Nehru University, New Delhi, India. His current research areas are: computer vision, image processing, object detection, pattern recognition, feature extraction, and classification. E-mail: [email protected]