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CNN-Based Approaches for Automatic Recognition of Turkish Sign Language

Year 2023, Volume: 13 Issue: 2, 760 - 777, 01.06.2023
https://doi.org/10.21597/jist.1223457

Abstract

Sign language is a nonverbal communication tool used by deaf and dumb individuals to convey their feelings, thoughts and social identities to their environment. Sign language has a key role in communication between deaf and dumb individuals and the rest of the society. Many sign language recognition systems have been developed with the increase in human-computer interaction and the fact that sign language is not widely known among normal people. In this study, a new number-based data set for Turkish sign language is proposed for the first time in the literature. The most up-to-date deep learning approaches have been applied to the proposed data set in order to classify Turkish sign language autonomously and to enable computer-based communication of people who have difficulties in this regard. The most up-to-date and popular architectures such as CNN-based VGG, ResNet, MobileNet, DenseNet and EfficientNet were used in the study. In experimental studies, it has been observed that the ResNet152 model performs better than other models with 98.76% accuracy, 98.85% precision, 98.81% sensitivity and 98.80% F1-score. Additionally, the other models used in experimental studies all show a success rate above 90%, supporting the effectiveness of the proposed data set. This shows that CNN models can successfully detect Turkish sign language.

References

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Türk İşaret Dilinin Sınıflandırılması için Derin Öğrenme Yaklaşımları

Year 2023, Volume: 13 Issue: 2, 760 - 777, 01.06.2023
https://doi.org/10.21597/jist.1223457

Abstract

İşaret dili, sağır ve dilsiz bireylerin duygularını, düşüncelerini ve sosyal kimliklerini çevrelerine aktarabilmek için kullandıkları sözsüz bir iletişim aracıdır. İşaret dili, sağır ve dilsiz bireyler ile toplumun geri kalan bireyleri arasındaki iletişimde kilit bir role sahiptir. Normal insanlar arasında işaret dilinin çok yaygın bilinmemesi ve insan-bilgisayar etkileşiminin artmasıyla birlikte birçok işaret dili tanıma sistemleri geliştirilmiştir. Bu çalışmada, Türk işaret dili için literatürde ilk kez rakam temelli yeni bir veri seti önerilmiştir. Türk işaret dilinin otonom bir şekilde sınıflandırılması ve bu konuda sıkıntı yaşayan insanların iletişimini bilgisayar temelli yapabilmesi için en güncel derin öğrenme yaklaşımları önerilen veri setine uygulanmıştır. Çalışmada özellikle CNN tabanlı VGG, ResNet, MobileNet, DenseNet ve EfficientNet gibi en güncel ve popüler mimariler kullanılmıştır. Deneysel çalışmalarda ResNet152 modeli, %98.76 doğruluk, %98.85 kesinlik, %98.81 duyarlılık ve %98.80 F1-skoru ile diğer modellere göre daha iyi performans gösterdiği gözlemlenmiştir. Ayrıca, deneysel çalışmalarda kullanılan diğer modellerin hepsi %90'ın üzerinde bir başarım oranı göstererek önerilen veri setinin etkililiğini desteklemektedir. Bu, CNN modellerinin Türk işaret dilini tanımayı başarılı bir şekilde tespit yapabildiğini göstermektedir.

References

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  • Al-Hammadi, M., Muhammad, G., Abdul, W., Alsulaiman, M., Bencherif, M. A., & Mekhtiche, M. A. (2020). Hand Gesture Recognition for Sign Language Using 3DCNN. IEEE Access, 8, 79491 - 79509.
  • Alici-Karaca, D., Akay, B., Yay, A., Suna, P., Nalbantoglu, O. U., Karaboga, D., . . . Baran, M. (2022). A new lightweight convolutional neural network for radiation-induced liver disease classification. Biomedical Signal Processing and Control, 73. doi:10.1016/j.bspc.2021.103463
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  • Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., & S.Lew, M. (2016). Deep learning for visual understanding: A review. Neurocomputing, 187, 27-48.
  • Gschwend, D. (2020). Zynqnet: An fpga-accelerated embedded convolutional neural network. arXiv preprint arXiv:2005.06892.
  • Halbouni, A., Gunawan, T. S., Habaebi, M. H., Halbouni, M., Kartiwi, M., & Ahmad, R. (2022). Machine Learning and Deep Learning Approaches for CyberSecurity: A Review. IEEE Access (10), 19572 - 19585. doi:10.1109/ACCESS.2022.3151248
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  • Justesen, N., Bontrager, P., Togelius, J., & Risi, S. (2020). Deep Learning for Video Game Playing. IEEE Transactions on Games, 12(1), 1 - 20.
  • Karaman, A., Karaboga, D., Pacal, I., Akay, B., Basturk, A., Nalbantoglu, U., Sahin, O. (2022). Hyper-parameter optimization of deep learning architectures using artificial bee colony (ABC) algorithm for high performance real-time automatic colorectal cancer (CRC) polyp detection. Applied Intelligence. https://doi.org/10.1007/s10489-022-04299-1
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  • Karagoz, M. A., Akay, B., Basturk, A., Karaboga, D., & Nalbantoglu, O. U. (2023). An unsupervised transfer learning model based on convolutional auto encoder for non-alcoholic steatohepatitis activity scoring and fibrosis staging of liver histopathological images. Neural Computing and Applications, 1-15.
  • Khari, M., Garg, A., Crespo, R. G., & Verdú, E. (2019). Gesture Recognition of RGB and RGB-D static Images using Convolutional Neural Networks. International Journal of Interactive Multimedia and Artificial Intelligence, 5(7), 22-27.
  • Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems 25.
  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
  • Li, Y., Ding, L., & Gao, X. (2018). On the Decision Boundary of Deep Neural Networks. https://arxiv.org/abs/1808.05385 adresinden alındı
  • Li, Z., Liu, F., Yang, W., Peng, S., & Zhou, J. (2021). A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects. IEEE Transactions on Neural Networks and Learning Systems, 33(12), 6999 - 7019.
  • LUQMAN, H., & ELALFY, E. (2022). Utilizing motion and spatial features for sign language gesture recognition using cascaded CNN and LSTM models. Turkish Journal of Electrical Engineering and Computer Sciences, 30(7), 2508-2525.
  • Ma, Y., Xu, T., & Kim, K. (2022). Two-Stream Mixed Convolutional Neural Network for American Sign Language Recognition. Sensors, 22(16), 5959.
  • Marais, M., Brown, D., Connan, J., & Boby, A. (2022). An Evaluation of Hand-Based Algorithms for Sign Language Recognition. 2022 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD). IEEE. doi:10.1109/icABCD54961.2022.9856310
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  • Naglot, D., & Kulkarni, M. (2016). Real time sign language recognition using the leap motion controller. International conference on inventive computation technologies (ICICT). 3, s. 1-5. IEEE.
  • Nam, Y., & Lee, C. (2021). Cascaded convolutional neural network architecture for speech emotion recognition in noisy conditions. Sensors, 21(13), 4399.
  • Núñez-Prieto, R., Gómez, P. C., & Liu, L. (2019, October). A real-time gesture recognition system with fpga accelerated zynqnet classification. In 2019 IEEE Nordic Circuits and Systems Conference (NORCAS): NORCHIP and International Symposium of System-on-Chip (SoC) (pp. 1-6). IEEE.
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  • PACAL, İ. (2022). Deep Learning Approaches for Classification of Breast Cancer in Ultrasound (US) Images. Journal of the Institute of Science and Technology, 1917–1927. https://doi.org/10.21597/jist.1183679
  • Pacal, I., & Karaboga, D. (2021). A robust real-time deep learning based automatic polyp detection system. Computers in Biology and Medicine, 134. https://doi.org/10.1016/J.COMPBIOMED.2021.104519
  • Pacal, I., Karaboga, D., Basturk, A., Akay, B., & Nalbantoglu, U. (2020). A comprehensive review of deep learning in colon cancer. Computers in Biology and Medicine, 126. https://doi.org/10.1016/J.COMPBIOMED.2020.104003
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There are 60 citations in total.

Details

Primary Language Turkish
Subjects Computer Software
Journal Section Bilgisayar Mühendisliği / Computer Engineering
Authors

Ishak Pacal 0000-0001-6670-2169

Melek Alaftekin 0000-0001-7440-1913

Early Pub Date May 27, 2023
Publication Date June 1, 2023
Submission Date December 23, 2022
Acceptance Date March 2, 2023
Published in Issue Year 2023 Volume: 13 Issue: 2

Cite

APA Pacal, I., & Alaftekin, M. (2023). Türk İşaret Dilinin Sınıflandırılması için Derin Öğrenme Yaklaşımları. Journal of the Institute of Science and Technology, 13(2), 760-777. https://doi.org/10.21597/jist.1223457
AMA Pacal I, Alaftekin M. Türk İşaret Dilinin Sınıflandırılması için Derin Öğrenme Yaklaşımları. J. Inst. Sci. and Tech. June 2023;13(2):760-777. doi:10.21597/jist.1223457
Chicago Pacal, Ishak, and Melek Alaftekin. “Türk İşaret Dilinin Sınıflandırılması için Derin Öğrenme Yaklaşımları”. Journal of the Institute of Science and Technology 13, no. 2 (June 2023): 760-77. https://doi.org/10.21597/jist.1223457.
EndNote Pacal I, Alaftekin M (June 1, 2023) Türk İşaret Dilinin Sınıflandırılması için Derin Öğrenme Yaklaşımları. Journal of the Institute of Science and Technology 13 2 760–777.
IEEE I. Pacal and M. Alaftekin, “Türk İşaret Dilinin Sınıflandırılması için Derin Öğrenme Yaklaşımları”, J. Inst. Sci. and Tech., vol. 13, no. 2, pp. 760–777, 2023, doi: 10.21597/jist.1223457.
ISNAD Pacal, Ishak - Alaftekin, Melek. “Türk İşaret Dilinin Sınıflandırılması için Derin Öğrenme Yaklaşımları”. Journal of the Institute of Science and Technology 13/2 (June 2023), 760-777. https://doi.org/10.21597/jist.1223457.
JAMA Pacal I, Alaftekin M. Türk İşaret Dilinin Sınıflandırılması için Derin Öğrenme Yaklaşımları. J. Inst. Sci. and Tech. 2023;13:760–777.
MLA Pacal, Ishak and Melek Alaftekin. “Türk İşaret Dilinin Sınıflandırılması için Derin Öğrenme Yaklaşımları”. Journal of the Institute of Science and Technology, vol. 13, no. 2, 2023, pp. 760-77, doi:10.21597/jist.1223457.
Vancouver Pacal I, Alaftekin M. Türk İşaret Dilinin Sınıflandırılması için Derin Öğrenme Yaklaşımları. J. Inst. Sci. and Tech. 2023;13(2):760-77.