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Review Papers | Electronics & Communication Engineering | India | Volume 10 Issue 4, April 2021 | Popularity: 4.9 / 10

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Review on CNN based Sign Language Recognition Methods

Akash Vijayan, Sajeena .A

Abstract: Fingerspelled sign learning is the initial stage of sign learning and moreover, are used when no corresponding sign exists or signer is not aware of it. Most of the existing tools for sign language learning use external sensors which are costly and unreliable. . The ASL, short for American Sign Language is the most widely used sign language across the globe with certain variations according to the country. Sign language recognition can improve the problem that the number of people who need to use sign language is large but the popularity of sign language is poor, and provide a more convenient way of study, work and life for people with hearing and language impairment. Hand locating and sign language recognition methods can generally be divided into traditional methods and deep learning methods. In recent years, with the brilliant achievements of deep learning in the field of computer vision, it has been proved that the method based on deep learning has many advantages, such as rich feature extraction, strong modeling ability and intuitive training. Therefore, this paper studies hand locating and sign language recognition of common sign language based on neural network. we proposed an efficient deep convolutional neural networks approach for hand gesture recognition. The proposed approach employed transfer learning to beat the scarcity of a large labeled hand gesture dataset.

Keywords: Fading, Interference, Wideband, Throughput, Received signal

Edition: Volume 10 Issue 4, April 2021

Pages: 1349 - 1355

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