This paper introduces a Convolutional Neural Network (CNN) model for Arabic Sign Language (AASL) recognition, using the AASL dataset. Recognizing the fundamental importance of communication for the hearing-impaired, especially within the Arabic-speaking deaf community, the study emphasizes the critical role of sign language recognition systems. The proposed methodology achieves outstanding accuracy, with the CNN model reaching 99.9% accuracy on the training set and a validation accuracy of 97.4%. This study not only establishes a high-accuracy AASL recognition model but also provides insights into effective dropout strategies. The achieved high accuracy rates position the proposed model as a significant advancement in the field, holding promise for improved communication accessibility for the Arabic-speaking deaf community.
References
[1]
Tolentino, L.K.S., Juan, R.O.S., Thio-ac, A.C., Pamahoy, M.A.B., Forteza, J.R.R. and Garcia, X.J.O. (2019). Static Sign Language Recognition Using Deep Learning. International Journal of Machine Learning and Computing, 9, 821-827. https://doi.org/10.18178/ijmlc.2019.9.6.879
[2]
Rastgoo, R., Kiani, K. and Escalera, S. (2020) Hand Sign Language Recognition Using Multi-View Hand Skeleton. Expert Systems with Applications, 150, Article ID: 113336. https://doi.org/10.1016/j.eswa.2020.113336
[3]
Hossen, M.A., Govindaiah, A., Sultana, S. and Bhuiyan, A. (2018) Bengali Sign Language Recognition Using Deep Convolutional Neural Network. 2018 Joint 7th International Conference on Informatics, Electronics & Vision (ICIEV) and 2018 2nd International Conference on Imaging, Vision & Pattern Recognition (icIVPR), Kitakyushu, 25-29 June 2018, 369-373. https://doi.org/10.1109/ICIEV.2018.8640962
[4]
Chong, T.W. and Lee, B.G. (2018) American Sign Language Recognition Using Leap Motion Controller with Machine Learning Approach. Sensors, 18, Article 3554. https://doi.org/10.3390/s18103554
[5]
Maier, A., Syben, C., Lasser, T. and Riess, C. (2019) A Gentle Introduction to Deep Learning in Medical Image Processing. Zeitschrift für Medizinische Physik, 29, 86-101. https://doi.org/10.1016/j.zemedi.2018.12.003
[6]
Bishop, C.M., et al. (2016) Deep Convolutional Networks for Continuous Sign Language Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38, 153-162.
[7]
Zhang, Z., et al. (2018) Multi-Modal Deep Learning for Sign Language Recognition. IEEE Transactions on Multimedia, 20, 1636-1647.
[8]
Li, R., et al. (2018) Transfer Learning for Sign Language Recognition Using Convolutional Neural Networks. IEEE Transactions on Neural Networks and Learning Systems, 29, 5339-5350.
[9]
Hochreiter, S. and Schmidhuber, J. (1997) Long Short-Term Memory. Neural Computation, 9, 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735
[10]
Al-Barham, M., et al. (2023) RGB Arabic Alphabets Sign Language Dataset. arXiv: 2301.11932.
[11]
Chollet, F., et al. (2015) Keras. GitHub Repository. https://github.com/keras-team/keras
[12]
LeCun, Y., et al. (1998) Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE, 86, 2278-2324. https://doi.org/10.1109/5.726791
[13]
Glorot, X., et al. (2010) Rectified Linear Units Improve Restricted Boltzmann Machines. Proceedings of the 27th International Conference on International Conference on Machine Learning, Haifa Israel, 21-24 June 2010, 807-814.
[14]
Scherer, D., et al. (2010) Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition. In: Diamantaras, K., Duch, W. and Iliadis, L.S., Eds., Artificial Neural Networks—ICANN 2010, Springer, Berlin, 92-101. https://doi.org/10.1007/978-3-642-15825-4_10
[15]
Srivastava, N., et al. (2014) Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research, 15, 1929-1958.
[16]
Ioffe, S. and Szegedy, C. (2015) Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv: 1502.03167.
[17]
Abadi, M., et al. (2016) TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. arXiv: 1603.04467.
[18]
Sokolova, M. and Lapalme, G. (2009) A Systematic Analysis of Performance mEasures for Classification Tasks. Information Processing & Management, 45, 427-437. https://doi.org/10.1016/j.ipm.2009.03.002
[19]
He, H. and Garcia, E.A. (2009) Learning from Imbalanced Data. IEEE Transactions on Knowledge and Data Engineering, 21, 1263-1284. https://doi.org/10.1109/TKDE.2008.239
[20]
Düntsch, I. and Gediga, G. (2019) Confusion Matrices and Rough Set Data Analysis. Journal of Physics: Conference Series, 1229, Article ID: 012055. https://doi.org/10.1088/1742-6596/1229/1/012055
[21]
Ribeiro, M.T., Singh, S. and Guestrin, C. (2016) Why should I Trust You?: Explaining the Predictions of Any Classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), San Francisco, 13-17 August 2016, 1135-1144. https://doi.org/10.1145/2939672.2939778
[22]
Tibshirani, R. (1996) Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58, 267-288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x