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Published in: Education and Information Technologies 6/2020

04-06-2020

Recognition system for alphabet Arabic sign language using neutrosophic and fuzzy c-means

Authors: Safaa M. Elatawy, Doaa M. Hawa, A. A. Ewees, Abeer M. Saad

Published in: Education and Information Technologies | Issue 6/2020

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Abstract

Sign language is considered as the important communication means among the normal people and the deaf. Therefore, developing communication systems to help those people is an important issue. In this paper, the neutrosophic technique and fuzzy c-means are applied to detect and recognize the alphabet Arabic sign language. The proposed system starts by using a gaussian filter to delete the noise and prepare the input image to be used in the next step. After that, the image is converted to the neutrosophic domain then its features are extracted to start the classification phase; then the corresponding letter is displayed in the proposed system. The results showed good performance for the proposed system whereas, the total classification accuracy reached 91% in the experiment.

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Literature
go back to reference Ahmed, A. M., Alez, R. A., Taha, M., & Tharwat, G. (2016). Automatic translation of Arabic sign to Arabic text (ATASAT) system. Journal of Computer Science and Information Technology, 6, 109–122. Ahmed, A. M., Alez, R. A., Taha, M., & Tharwat, G. (2016). Automatic translation of Arabic sign to Arabic text (ATASAT) system. Journal of Computer Science and Information Technology, 6, 109–122.
go back to reference Ahmed, A. M. et al. (2017). “Towards the design of automatic translation system from Arabic Sign Language to Arabic text,” In International Conference on Inventive Computing and Informatics (ICICI), 2017, pp. 325–330. Ahmed, A. M. et al. (2017). “Towards the design of automatic translation system from Arabic Sign Language to Arabic text,” In International Conference on Inventive Computing and Informatics (ICICI), 2017, pp. 325–330.
go back to reference Alam, M. S., et al. (2019). Automatic Human Brain Tumor Detection in MRI Image Using Template-Based K Means and Improved Fuzzy C Means Clustering Algorithm. Big Data and Cognitive Computing, 3(2), 27.CrossRef Alam, M. S., et al. (2019). Automatic Human Brain Tumor Detection in MRI Image Using Template-Based K Means and Improved Fuzzy C Means Clustering Algorithm. Big Data and Cognitive Computing, 3(2), 27.CrossRef
go back to reference Aliyu, S., Mohandes, M., Deriche, M., and Badran, S. (2016), “Arabie sign language recognition using the Microsoft Kinect,” In 2016 13th International Multi-Conference on Systems, Signals & Devices (SSD), pp. 301–306. Aliyu, S., Mohandes, M., Deriche, M., and Badran, S. (2016), “Arabie sign language recognition using the Microsoft Kinect,” In 2016 13th International Multi-Conference on Systems, Signals & Devices (SSD), pp. 301–306.
go back to reference Almohimeed, A., Wald, M., and Damper, R. (2010), “An Arabic Sign Language corpus for instructional language in school,” In LREC 2010: 4th Workshop on the Representation and Processing of Sign Languages: Corpora and Sign Language Technologies, pp. 81–82. Almohimeed, A., Wald, M., and Damper, R. (2010), “An Arabic Sign Language corpus for instructional language in school,” In LREC 2010: 4th Workshop on the Representation and Processing of Sign Languages: Corpora and Sign Language Technologies, pp. 81–82.
go back to reference Almohimeed, A., Wald, M., and Damper, R. I. (2011), “Arabic text to Arabic sign language translation system for the deaf and hearing-impaired community,” In Proceedings of the Second Workshop on Speech and Language Processing for Assistive Technologies, pp. 101–109. Almohimeed, A., Wald, M., and Damper, R. I. (2011), “Arabic text to Arabic sign language translation system for the deaf and hearing-impaired community,” In Proceedings of the Second Workshop on Speech and Language Processing for Assistive Technologies, pp. 101–109.
go back to reference Aly, S., Osman, B., Aly, W., and Saber, M. (2016). “Arabic sign language fingerspelling recognition from depth and intensity images,” In 2016 12th International Computer Engineering Conference (ICENCO), pp. 99–104. Aly, S., Osman, B., Aly, W., and Saber, M. (2016). “Arabic sign language fingerspelling recognition from depth and intensity images,” In 2016 12th International Computer Engineering Conference (ICENCO), pp. 99–104.
go back to reference Cassenti, D. N. (2018). Advances in human factors in simulation and modeling. Springer. Cassenti, D. N. (2018). Advances in human factors in simulation and modeling. Springer.
go back to reference Eisa, M. M., Ewees, A. A., Refaat, M. M., & Elgamal, A. F. (2013). Effective medical image retrieval technique based on texture features. International Journal of Intelligent Computing and Information Science, 13(2), 19–33. Eisa, M. M., Ewees, A. A., Refaat, M. M., & Elgamal, A. F. (2013). Effective medical image retrieval technique based on texture features. International Journal of Intelligent Computing and Information Science, 13(2), 19–33.
go back to reference El Alfi, A. E. E., & Atawy, S. (2018). Intelligent Arabic sign language to Arabic text translation for easy deaf communication. International Journal of Computers and Applications, 975, 8887. El Alfi, A. E. E., & Atawy, S. (2018). Intelligent Arabic sign language to Arabic text translation for easy deaf communication. International Journal of Computers and Applications, 975, 8887.
go back to reference Elpeltagy, M., Abdelwahab, M., Hussein, M. E., Shoukry, A., Shoala, A., & Galal, M. (2018). Multi-modality-based Arabic sign language recognition. IET Computer Vision, 12(7), 1031–1039.CrossRef Elpeltagy, M., Abdelwahab, M., Hussein, M. E., Shoukry, A., Shoala, A., & Galal, M. (2018). Multi-modality-based Arabic sign language recognition. IET Computer Vision, 12(7), 1031–1039.CrossRef
go back to reference Eser, S., & Derya, A. (2019). A new edge detection approach via neutrosophy based on maximum norm entropy. Expert Systems with Applications, 115, 499–511.CrossRef Eser, S., & Derya, A. (2019). A new edge detection approach via neutrosophy based on maximum norm entropy. Expert Systems with Applications, 115, 499–511.CrossRef
go back to reference Ewees, A. A., Elaziz, M. A., & Oliva, D. (2018). Image segmentation via multilevel thresholding using hybrid optimization algorithms. Journal of Electronic Imaging, 27(6), 63008.CrossRef Ewees, A. A., Elaziz, M. A., & Oliva, D. (2018). Image segmentation via multilevel thresholding using hybrid optimization algorithms. Journal of Electronic Imaging, 27(6), 63008.CrossRef
go back to reference Ewees, A. A., ELLaban, H. A., and ElEraky, R. M. (2019). “Features Selection for Facial Expression Recognition,” in In the 10th Int. Conf. on computing, communication and networking technologies(ICCCNT). Ewees, A. A., ELLaban, H. A., and ElEraky, R. M. (2019). “Features Selection for Facial Expression Recognition,” in In the 10th Int. Conf. on computing, communication and networking technologies(ICCCNT).
go back to reference Gaheen, M. A., Ewees, A. A., and Farouk, F. (2019). “Face-Pose Estimation for Learning Systems,” In 10th international conference on computing, Communication and Networking Technologies (ICCCNT), 2019, pp. 1–6. Gaheen, M. A., Ewees, A. A., and Farouk, F. (2019). “Face-Pose Estimation for Learning Systems,” In 10th international conference on computing, Communication and Networking Technologies (ICCCNT), 2019, pp. 1–6.
go back to reference Gaheen, M. A., Ewees, A. A., and Eisa, M. (2020). “Students Head-Pose Estimation Using Partially-Latent Mixture,” In Emerging Trends in Electrical, Communications, and Information Technologies, Springer, pp. 717–729. Gaheen, M. A., Ewees, A. A., and Eisa, M. (2020). “Students Head-Pose Estimation Using Partially-Latent Mixture,” In Emerging Trends in Electrical, Communications, and Information Technologies, Springer, pp. 717–729.
go back to reference Guesmi, F., Bouchrika, T., Jemai, O., Zaied, M., and Ben Amar, C. (2016). “Arabic sign language recognition system based on wavelet networks,” In 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 3561–3566. Guesmi, F., Bouchrika, T., Jemai, O., Zaied, M., and Ben Amar, C. (2016). “Arabic sign language recognition system based on wavelet networks,” In 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 3561–3566.
go back to reference Hisham, B., & Hamouda, A. (2017). Arabic static and dynamic gestures recognition using leap motion. Journal of Computer Science, 13(8), 337–354.CrossRef Hisham, B., & Hamouda, A. (2017). Arabic static and dynamic gestures recognition using leap motion. Journal of Computer Science, 13(8), 337–354.CrossRef
go back to reference Hooda, H., Verma, O. P., and Singhal, T. (2014). “Brain tumor segmentation: A performance analysis using K-Means, Fuzzy C-Means and Region growing algorithm,” In 2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies, pp. 1621–1626. Hooda, H., Verma, O. P., and Singhal, T. (2014). “Brain tumor segmentation: A performance analysis using K-Means, Fuzzy C-Means and Region growing algorithm,” In 2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies, pp. 1621–1626.
go back to reference Houssein, E. H., Ewees, A. A., & ElAziz, M. A. (2018). Improving twin support vector machine based on hybrid swarm optimizer for heartbeat classification. Pattern Recognition and Image Analysis, 28(2), 243–253.CrossRef Houssein, E. H., Ewees, A. A., & ElAziz, M. A. (2018). Improving twin support vector machine based on hybrid swarm optimizer for heartbeat classification. Pattern Recognition and Image Analysis, 28(2), 243–253.CrossRef
go back to reference Ibrahim, R. A., Elaziz, M. A., Ewees, A. A., Selim, I. M., & Lu, S. (2018). Galaxy images classification using hybrid brain storm optimization with moth flame optimization. Journal of Astronomical Telescopes, Instruments, and Systems, 4(3), 38001.CrossRef Ibrahim, R. A., Elaziz, M. A., Ewees, A. A., Selim, I. M., & Lu, S. (2018). Galaxy images classification using hybrid brain storm optimization with moth flame optimization. Journal of Astronomical Telescopes, Instruments, and Systems, 4(3), 38001.CrossRef
go back to reference Ibrahim, E., Ewees, A. A., and Eisa, M. (2020). “Proposed Method for Segmenting Skin Lesions Images,” In Emerging Trends in Electrical, Communications, and Information Technologies, Springer, pp. 13–23. Ibrahim, E., Ewees, A. A., and Eisa, M. (2020). “Proposed Method for Segmenting Skin Lesions Images,” In Emerging Trends in Electrical, Communications, and Information Technologies, Springer, pp. 13–23.
go back to reference Luqman, H., Mahmoud, S. A., et al. (2017). Transform-based Arabic sign language recognition. Procedia Computer Science, 117, 2–9.CrossRef Luqman, H., Mahmoud, S. A., et al. (2017). Transform-based Arabic sign language recognition. Procedia Computer Science, 117, 2–9.CrossRef
go back to reference S. A. Mane and K. V Kulhalli, “Mammogram image features extraction and classification for breast Cancer detection,” International Research Journal of Engineering and Technology , vol. 2, no. 7, pp. 810–814, 2015. S. A. Mane and K. V Kulhalli, “Mammogram image features extraction and classification for breast Cancer detection,” International Research Journal of Engineering and Technology , vol. 2, no. 7, pp. 810–814, 2015.
go back to reference Maraqa, M., Al-Zboun, F., Dhyabat, M., & Zitar, R. A. (2012). Recognition of Arabic sign language (ArSL) using recurrent neural networks. Journal of Intelligent Learning Systems and Applications, 4(01), 41.CrossRef Maraqa, M., Al-Zboun, F., Dhyabat, M., & Zitar, R. A. (2012). Recognition of Arabic sign language (ArSL) using recurrent neural networks. Journal of Intelligent Learning Systems and Applications, 4(01), 41.CrossRef
go back to reference Moghaddam, R. F., & Cheriet, M. (2012). AdOtsu: An adaptive and parameterless generalization of Otsu’s method for document image binarization. Pattern Recognition, 45(6), 2419–2431.CrossRef Moghaddam, R. F., & Cheriet, M. (2012). AdOtsu: An adaptive and parameterless generalization of Otsu’s method for document image binarization. Pattern Recognition, 45(6), 2419–2431.CrossRef
go back to reference Mohandes, M. A. (2013). Recognition of two-handed Arabic signs using the CyberGlove. Arabian Journal for Science and Engineering, 38(3), 669–677.CrossRef Mohandes, M. A. (2013). Recognition of two-handed Arabic signs using the CyberGlove. Arabian Journal for Science and Engineering, 38(3), 669–677.CrossRef
go back to reference Mohandes, M., Aliyu, S., and Deriche, M. (2014). “Arabic sign language recognition using the leap motion controller,” In 2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE), pp. 960–965. Mohandes, M., Aliyu, S., and Deriche, M. (2014). “Arabic sign language recognition using the leap motion controller,” In 2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE), pp. 960–965.
go back to reference Nandan, D., Kanungo, J., & Mahajan, A. (2018). An error-efficient Gaussian filter for image processing by using the expanded operand decomposition logarithm multiplication. Journal of Ambient Intelligence and Humanized Computing, 1–8. Nandan, D., Kanungo, J., & Mahajan, A. (2018). An error-efficient Gaussian filter for image processing by using the expanded operand decomposition logarithm multiplication. Journal of Ambient Intelligence and Humanized Computing, 1–8.
go back to reference Sahlol, A. T., Kollmannsberger, P., & Ewees, A. A. (2020). Efficient classification of white blood cell leukemia with improved swarm optimization of deep features. Scientific Reports, 10(1), 1–11.CrossRef Sahlol, A. T., Kollmannsberger, P., & Ewees, A. A. (2020). Efficient classification of white blood cell leukemia with improved swarm optimization of deep features. Scientific Reports, 10(1), 1–11.CrossRef
go back to reference Shasidhar, M., Raja, V. S., and Kumar, B. V. (2011). “MRI brain image segmentation using modified fuzzy c-means clustering algorithm,” In 2011 International Conference on Communication Systems and Network Technologies, pp. 473–478. Shasidhar, M., Raja, V. S., and Kumar, B. V. (2011). “MRI brain image segmentation using modified fuzzy c-means clustering algorithm,” In 2011 International Conference on Communication Systems and Network Technologies, pp. 473–478.
go back to reference Tharwat, A., Gaber, T., Hassanien, A. E., Shahin, M. K., and Refaat, B. (2015). “Sift-based arabic sign language recognition system,” In Afro-european conference for industrial advancement, pp. 359–370. Tharwat, A., Gaber, T., Hassanien, A. E., Shahin, M. K., and Refaat, B. (2015). “Sift-based arabic sign language recognition system,” In Afro-european conference for industrial advancement, pp. 359–370.
go back to reference Zhang, M., Zhang, L., & Cheng, H.-D. (2010). A neutrosophic approach to image segmentation based on watershed method. Signal Processing, 90(5), 1510–1517.MATHCrossRef Zhang, M., Zhang, L., & Cheng, H.-D. (2010). A neutrosophic approach to image segmentation based on watershed method. Signal Processing, 90(5), 1510–1517.MATHCrossRef
Metadata
Title
Recognition system for alphabet Arabic sign language using neutrosophic and fuzzy c-means
Authors
Safaa M. Elatawy
Doaa M. Hawa
A. A. Ewees
Abeer M. Saad
Publication date
04-06-2020
Publisher
Springer US
Published in
Education and Information Technologies / Issue 6/2020
Print ISSN: 1360-2357
Electronic ISSN: 1573-7608
DOI
https://doi.org/10.1007/s10639-020-10184-6

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