Arabic Sign Language Recognition: A Review

Öz

Sign language is the language used by deaf people to communicate with one another and with common people. An interpreter is typically required when an ordinary person wishes to communicate with a deaf person. Visual sign language recognition is a dynamic area of research in computer vision. A functional sign language recognition (SLR) system can provide an opportunity for a deaf person to communicate with non-signed persons without the need for an interpreter. It can be used to produce speech or text that makes the mute more autonomous. Arabic Sign Language Recognition (ArSLR) is an important area of study. It helps to eliminate barriers between the deaf and the community. This paper includes a review of the Arabic Sign Language Recognition System (ArSLRS). Present challenges and future research opportunities are also illustrated.

Anahtar Kelimeler

• Sign Language (SL),
• Arabic Sign Language (ArSL)
• Arabic Sign Language Recognition (ArSLR)

Arapça İşaret Dili Tanıma: Bir İnceleme

Öz

İşaret dili, sağır insanların birbirleriyle ve sıradan insanlarla iletişim kurmak için kullandıkları dildir. Sıradan bir kişi sağır bir kişiyle iletişim kurmak istediğinde genellikle bir tercüman gerekmektedir. Görsel işaret dili tanıma, bilgisayarla görmede dinamik bir araştırma alanıdır. İşlevsel bir işaret dili tanıma sistemi, işitme engelli bir kişiye, bir tercümana ihtiyaç duymadan imzasız kişilerle iletişim kurma fırsatı sağlamaktadır. Arap İşaret Dili Tanıma (ARSLR) önemli bir çalışma alanıdır. Sağırlar ve toplum arasındaki engelleri ortadan kaldırmaya yardımcı olmaktadır. Sağır kişiyi daha özerk yapan konuşma veya metin üretmek için de kullanılmaktadır. Bu makale Arap İşaret Dili Tanıma Sisteminin bir incelemesini içermektedir. Mevcut zorluklar ve gelecekteki araştırma fırsatları da gösterilmektedir.

Anahtar Kelimeler

• İşaret Dili (SL),
• Arap İşaret Dili (ArSL),
• Arap İşaret Dili Tanıma (ArSLR)

Kaynakça

1. [1] M. Abdel-Fattah, "Arabic Sign Language: A Perspective", Journal of Deaf Studies and Deaf Education, vol. 10, no. 2, pp. 212-221, 2005. Available: 10.1093/deafed/eni007.
2. [2] Al-Ahdal, M. Ebrahim, and Md Tahir Nooritawati, Review in sign language recognition systems. In 2012 IEEE Symposium on Computers & Informatics (ISCI), pp. 52-57. IEEE, 2012.
3. [3] O. Al-Jarrah and A. Halawani, "Recognition of gestures in Arabic sign language using neuro-fuzzy systems", Artificial Intelligence, vol. 133, no. 1-2, pp. 117-138, 2001. Available: 10.1016/s0004-3702(01)00141-2.
4. [4] A., Alnahhas, B., Alkhatib, N., Al-Boukaee, N., Alhakim, O., Alzabibi and N., Ajalyakeen , "Deep Learning based Dynamic Hand Gesture Recognition with Leap Motion Controller", International Journal of Advanced Trends in Computer Science and Engineering, vol. 9, no. 5, pp. 7309-7315, 2020. Available: 10.30534/ijatcse/2020/61952020.
5. [5] M. AL-Rousan, K. Assaleh and A. Tala’a, "Video-based signer-independent Arabic sign language recognition using hidden Markov models", Applied Soft Computing, vol. 9, no. 3, pp. 990-999, 2009. Available: 10.1016/j.asoc.2009.01.002.
6. [6] A.S., Al-Shamayleh, R., Ahmad, N., Jomhari, and M.A., Abushariah, “AUTOMATIC ARABIC SIGN LANGUAGE RECOGNITION: A REVIEW, TAXONOMY, OPEN CHALLENGES, RESEARCH ROADMAP AND FUTURE DIRECTIONS”, Malaysian Journal of Computer Science, vol. 33, no.4, pp.306-343, 2020.
7. [7] R., Alzohairi, R., Alghonaim, W., Alshehri, S., Aloqeely, M., Alzaidan and O., Bchir, “Image based Arabic sign language recognition system”, International Journal of Advanced Computer Science and Applications (IJACSA), vol. 9, no. 3, 2018.
8. [8] K., Assaleh, T., Shanableh, M., Fanaswala, F., Amin and H., Bajaj, “Continuous Arabic sign language recognition in user dependent mode”, 2010.

9. [9] M.J., Cheok, Z., Omar and M.H., Jaward, “A review of hand gesture and sign language recognition techniques”, International Journal of Machine Learning and Cybernetics, vol. 10 no. 1, pp.131-153, 2019.
10. [10] A.S., Elons, M., Abull-Ela and M.F., Tolba, “A proposed PCNN features quality optimization technique for pose-invariant 3D Arabic sign language recognition”, Applied Soft Computing, vol. 13, no.4, pp.1646-1660, 2013.
11. [11] A.S., Elons, M., Abull-ela and M.F., Tolba, “Neutralizing lighting non-homogeneity and background size in PCNN image signature for Arabic Sign Language recognition”, Neural Computing and Applications, vol. 22, no. 1, pp.47-53, 2013.
12. [12] S.M. Halawani and A.B., Zaitun, “An avatar based translation system from arabic speech to arabic sign language for deaf people”, International Journal of Information Science and Education, vol. 2, no. 1,, pp.13-20, 2012.
13. [13] B., Hisham and A., Hamouda, “Arabic sign language recognition using Ada-Boosting based on a leap motion controller”, International Journal of Information Technology, pp.1-14, 2020.
14. [14] N.B., Ibrahim, M.M., Selim and H.H., Zayed, “An automatic arabic sign language recognition system (ArSLRS)”, Journal of King Saud University-Computer and Information Sciences, vol. 30, no. 4, pp.470-477, 2018.
15. [15] M.M., Kamruzzaman, “Arabic Sign Language Recognition and Generating Arabic Speech Using Convolutional Neural Network”, Wireless Communications and Mobile Computing, 2020.
16. [16] G., Latif, N., Mohammad, R., AlKhalaf, R., AlKhalaf, J., Alghazo and M., Khan, “An Automatic Arabic Sign Language Recognition System based on Deep CNN: An Assistive System for the Deaf and Hard of Hearing”, International Journal of Computing and Digital Systems, vol. 9, no. 4,, pp. 715-724, 2020.
17. [17] H., Luqman, and S.A., Mahmoud, “Transform-based Arabic sign language recognition”, Procedia Computer Science, vol. 117, pp.2-9, 2017.
18. [18] Miniwatts Marketing Group, “Internet World Users by Language”, 2020. https​://www.inter​netwo​rldst​ats.com/stats​7.htm, updated 11 Nov 2019. [Accessed: 11-Feb-2021]
19. [19] M. M., Mohamed, “Automatic system for Arabic sign language recognition and translation to spoken one”, International Journal of Advanced Trends in Computer Science and Engineering, vol. 9, no. 4, 7140–7148, 2020.
20. [20] M., Mohandes, M., Deriche, U., Johar and S., Ilyas, “A signer-independent Arabic Sign Language recognition system using face detection, geometric features, and a Hidden Markov Model”, Computers & Electrical Engineering, vol. 38, no. 2, pp. 422-433, 2012.
21. [21] J., Murray, “World Federation of the deaf”, 2018. Rome, Italy. Retrieved from http://wfdeaf.org/our-work/. [Accessed: 11-Feb-2021].
22. [22] M., Mustafa, “A study on Arabic sign language recognition for differently abled using advanced machine learning classifiers”, Journal of Ambient Intelligence and Humanized Computing, pp.1-15, 2020.
23. [23] M., Mustafa, H. , AbdAlla and H., Suleman, “Current approaches in Arabic IR: A survey”, in International Conference on Asian Digital Libraries, pp. 406-407, 2008, December. Springer, Berlin, Heidelberg.
24. [24] M., Mustafa, A.S., Eldeen, S., Bani-Ahmad and A.O., Elfaki, “A comparative survey on arabic stemming: approaches and challenges”, Intelligent Information Management, vol. 9, no. 2, p. 39, 2017.
25. [25] S., Reshna, and M., Jayaraju, “Indian Sign Language Recognition System–A Review”, in International Conference on Signal and Speech Processing, ICSSP, vol. 14, 2014.
26. [26] Y., Saleh, and G., Issa, “Arabic Sign Language Recognition through Deep Neural Networks Fine-Tuning”, 2020.
27. [27] T., Shanableh, and K., Assaleh, “User-independent recognition of Arabic sign language for facilitating communication with the deaf community”, Digital Signal Processing, vol. 21, no.4, pp.535-542, 2011.
28. [28] V., Sharma, V., Kumar, S.C., Masaguppi, M.N., Suma and D.R., Ambika, “Virtual talk for deaf, mute, blind and normal humans”, in 2013 Texas Instruments India Educators' Conference, pp. 316-320, 2013, April. IEEE.
29. [29] P., Shukla, A., Garg, K., Sharma, and A., Mittal, “A DTW and Fourier Descriptor based approach for Indian Sign Language recognition”, in 2015 Third International Conference on Image Information Processing (ICIIP), pp. 113-118, 2015, December.. IEEE.
30. [30] M.F., Tolba, M.S., Abdellwahab, M., Aboul-Ela, and A., Samir, “Image signature improving by PCNN for Arabic sign language recognition”, Canadian Journal on Artificial Intelligence, Machine Learning & Pattern Recognition, vol. 1, no. 1, pp.1-6, 2010.
31. [31] M.F., Tolba, A., Samir and M., Aboul-Ela, “Arabic sign language continuous sentences recognition using PCNN and graph matching”, Neural Computing and Applications, vol. 23, no.3-4, pp. 999-1010, 2013.
32. [32] A., Wadhawan and P., Kumar, “Sign Language Recognition Systems: A Decade Systematic Literature Review”, Archives of Computational Methods in Engineering, pp.1-29, 2019.
33. [33] S., Wei, X., Chen, X., Yang, S. Cao, and X., Zhang, “A component-based vocabulary-extensible sign language gesture recognition framework”, Sensors, vol. 16, no. 4, p. 556, 2016.