BISINDO (Bahasa Isyarat Indonesia) Sign Language Recognition Using Deep Learning
Abstract
Sign language recognition plays a crucial role in
facilitating communication for individuals with hearing
impairments. This paper presents a deep learning-based
approach for recognizing Bahasa Isyarat Indonesia (BISINDO),
the sign language used in Indonesia. The proposed system
employs convolutional neural networks (CNNs) and recurrent
neural networks (RNNs) to automatically extract features from
sign language gestures and classify them into corresponding
linguistic units. The dataset used for training and evaluation
consists of annotated BISINDO sign language videos.
Preprocessing techniques such as normalization and
augmentation are applied to enhance the robustness of the
model. Experimental results demonstrate the effectiveness of the
proposed approach in accurately recognizing BISINDO sign
language gestures, achieving state-of-the-art performance
compared to existing methods. The developed system shows
promising potential for real-world applications in enhancing
communication accessibility for the hearing-impaired
community in Indonesia.
Keywords
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REFERENCES
Isma, S. T. P. (2012). Signing varieties in Jakarta and
Yogyakarta: Dialects or separate languages. Master of Art Thesis,
The Chinese University of Hong Kong.
Perumal, S., & Velmurugan, T. (2018). Preprocessing by
contrast enhancement techniques for medical images. International
Journal of Pure and Applied Mathematics, 118(18), 3681–3688.
Solichin, A., & Harjoko, A. (2013). Metode Background
Subtraction untuk Deteksi Obyek Pejalan Kaki pada
Lingkungan Statis. Seminar Nasional Teknologi Informasi 2013,
–6.
Alginahi, Y. (2010). Preprocessing Techniques in Character
Recognition. Intech, 10, 1–21. https://doi.org/10.5772/9776.
Avola, D., Bernardi, M., Cinque, L., Foresti, G.L., &
Massaroni, C. (2019). Exploiting Recurrent Neural Networks
and Leap Motion Controller for the Recognition of Sign
Language and Semaphoric Hand Gestures. IEEE Transactions
on Multimedia, 21(1), 234–245.
https://doi.org/10.1109/TMM.2018.2856094.
Yang, S., & Zhu, Q. (2017). Continuous Chinese sign
language recognition with CNN-LSTM. Ninth International
Conference on Digital Image Processing (ICDIP 2017),
(100), 104200F. https://doi.org/10.1117/12.2281671.
Bindu, G.H., Reddy, P.V.G.D. Prasad, & Murty, M.
Ramakrishna. (2018). Image preprocessing of abdominal CT
scan to improve visibility of any lesions in kidneys. Journal of
Theoretical and Applied Information Technology, 96(8), 2298–
Karambakhsh, A., Kamel, A., Sheng, B., Li, P., Yang, P., &
Feng, D.D. (2019). Deep gesture interaction for augmented
anatomy learning. International Journal of Information
Management, 45(October 2017), 328–336.
https://doi.org/10.1016/j.ijinfomgt.2018.03.004.
Ariesta, M.C., Wiryana, F., Suharjito, & Zahra, A. (2018).
Sentence Level Indonesian Sign Language Recognition Using
D Convolutional Neural Network and Bidirectional Recurrent
Neural Network. 2018 Indonesian Association for Pattern
Recognition International Conference (INAPR), 16–22.
https://doi.org/10.1109/INAPR.2018.8627016.
Molchanov, P., Gupta, S., Kim, K., & Kautz, J. (2015).
Hand gesture recognition with 3D convolutional neural
networks. Proceedings of the IEEE Conference on Computer
Vision and Pattern Recognition Workshops, 1–7.
https://doi.org/10.1109/CVPRW.2015.7301342.
Syulistyo, A. R., Hormansyah, D. S., & Saputra, P. Y.
(2020). SIBI (Sistem Isyarat Bahasa Indonesia) translation
using Convolutional Neural Network (CNN). IOP Conference
Series: Materials Science and Engineering, 732(1), 012082.
https://doi.org/10.1088/1757-899X/732/1/012082.
Köpüklü, O., Gunduz, A., Kose, N., & Rigoll, G. (2019).
DOI: http://dx.doi.org/10.33021/itfs.v9i1.5076
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