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

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.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

References

  1. 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.

    Google Scholar 

  2. 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.

  3. 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.

    Google Scholar 

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. Cassenti, D. N. (2018). Advances in human factors in simulation and modeling. Springer.

  9. 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.

    Google Scholar 

  10. 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.

    Google Scholar 

  11. 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.

    Google Scholar 

  12. Eser, S., & Derya, A. (2019). A new edge detection approach via neutrosophy based on maximum norm entropy. Expert Systems with Applications, 115, 499–511.

    Google Scholar 

  13. 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.

    Google Scholar 

  14. 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).

  15. 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.

  16. 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.

  17. 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.

  18. Hisham, B., & Hamouda, A. (2017). Arabic static and dynamic gestures recognition using leap motion. Journal of Computer Science, 13(8), 337–354.

    Google Scholar 

  19. 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.

  20. 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.

    Google Scholar 

  21. 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.

    Google Scholar 

  22. 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.

  23. Luqman, H., Mahmoud, S. A., et al. (2017). Transform-based Arabic sign language recognition. Procedia Computer Science, 117, 2–9.

    Google Scholar 

  24. 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.

  25. 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.

    Google Scholar 

  26. 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.

    Google Scholar 

  27. Mohandes, M. A. (2013). Recognition of two-handed Arabic signs using the CyberGlove. Arabian Journal for Science and Engineering, 38(3), 669–677.

    Google Scholar 

  28. 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.

  29. 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.

  30. 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.

    Google Scholar 

  31. 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.

  32. 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.

  33. Zhang, M., Zhang, L., & Cheng, H.-D. (2010). A neutrosophic approach to image segmentation based on watershed method. Signal Processing, 90(5), 1510–1517.

    MATH  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Safaa M. Elatawy.

Ethics declarations

Declarations of interest

There is no conflict of interest in this paper.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Elatawy, S.M., Hawa, D.M., Ewees, A.A. et al. Recognition system for alphabet Arabic sign language using neutrosophic and fuzzy c-means. Educ Inf Technol (2020). https://doi.org/10.1007/s10639-020-10184-6

Download citation

Keywords

  • Arabic sign language recognition
  • Neutrosophic
  • Image processing
  • Fuzzy c-means