Abstract
Sign language acts as a mediator for deaf and speech-impaired community to visually communicate with one another and engage in their environment. As a state-of-the-art recognition technique in computer vision, deep learning models have demonstrated success for several tasks. In this paper, we present a Convolutional Neural Network (CNN) approach with transfer learning to recognize Arabic and American Sign Languages alphabets with complex background. The descent underlying concept of transfer learning is to adopt a model pretrained on a massive annotated dataset and fine tune the later layers on the target dataset. We applied different techniques to improve the accuracy of the proposed approach such as data augmentation, batch-normalization, and early stopping. The proposed model is evaluated on three datasets and experiments reveal improved results with high recognition rates.
Original language | English |
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Title of host publication | 2020 International Conference on Innovation and Intelligence for Informatics, Computing and Technologies, 3ICT 2020 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781728196732 |
DOIs | |
State | Published - 20 Dec 2020 |
Publication series
Name | 2020 International Conference on Innovation and Intelligence for Informatics, Computing and Technologies, 3ICT 2020 |
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Bibliographical note
Publisher Copyright:© 2020 IEEE.
Keywords
- Convolutional neural network.
- Deep learning
- Gesture recognition
- Sign language recognition
- Transfer learning
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Science Applications
- Information Systems