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R-DCNN Based Automatic Recognition of Indian Sign Language

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Intelligent Systems Design and Applications (ISDA 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 418))

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Abstract

An implementation of a system that can recognize the sign language in real time would be highly beneficial to the deaf and dumb community. The proposed work aims to develop a system for automatic recognition of hand gestures pertaining to finger spelling in the Indian sign language System (ISL) and thereby overcome the barrier that the disabled faces to communicate. The proposed R-DCNN automatic recognition is the combination of Region of Interest (ROI) and deep convolution neural network (DCNN) layers. Dataset is created with 26 alphabets and 9 numbers of 1200 gestures for each and hence 42,000 gestures are trained. The system proposes to use Three Conv2d layers followed by one dense as the hidden layer. Finally, one dense layer containing 35 nodes is used for recognizing the gesture. The time consuming for training in CNN is overcome by applying Region of Interest algorithm. Hence, the combination of neural networks gives highly efficient and accurate system for sign language recognition in real time.

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Author information

Authors and Affiliations

  1. B.S A Crescent Institute of Science and Technology, Chennai, Tamil Nadu, India

    S. Subhashini & S. Revathi

  2. St. Joseph College of Engineering, Chennai, Tamil Nadu, India

    S. Shanthini

Authors
  1. S. Subhashini
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  2. S. Revathi
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  3. S. Shanthini
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Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  2. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Niketa Gandhi

  3. Institut für Wirtschaftsinformatik, Fachhochschule Nordwestschweiz, Olten, Switzerland

    Thomas Hanne

  4. Department of Computer Science and information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  5. Federal University of Bahia, Ondina, Brazil

    Tatiane Nogueira Rios

  6. Nantong University, Nantong Shi, Jiangsu, China

    Weiping Ding

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Subhashini, S., Revathi, S., Shanthini, S. (2022). R-DCNN Based Automatic Recognition of Indian Sign Language. In: Abraham, A., Gandhi, N., Hanne, T., Hong, TP., Nogueira Rios, T., Ding, W. (eds) Intelligent Systems Design and Applications. ISDA 2021. Lecture Notes in Networks and Systems, vol 418. Springer, Cham. https://doi.org/10.1007/978-3-030-96308-8_69

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