Proceedings of International Conference on Applied Innovation in IT
2025/06/27, Volume 13, Issue 2, pp.135-144

Arabic Sign Language Hand Gesture Recognition Using the Support Vector Machine Algorithm


Hind I. Mohammed, Sabah A. Abdulkareem, Mustafa N. Ghazal, Md. Rokonuzzaman and Nuha S.Mohammed


Abstract: Arabic Sign Language (ArSL) plays crucial role in facilitating communication for hearing-impaired community in Arabic-speaking countries and hand gesture recognition systems can contribute to improving accessibility and enabling communication and communication with them. Hand gesture recognition (HGR) has wide range of applications, including virtual environments, intelligent monitoring, sign language interpretation, medical systems, etc. Translating Arabic Sign Language using hand gestures and machine learning (ML) algorithms is one of the most important applications we have created. To develop a system for recognizing hand gestures in Arabic Sign Language using SVM, which is one of the widely used machine learning techniques? To develop a powerful classifier for hand gesture recognition By training the model to improve the hyper-level to effectively separate different classes of hand gestures based on the extracted features and evaluating the performance of the classifier using different evaluation metrics to determine its accuracy and generalization capabilities, we need dataset of hand gesture Samples labeled with their corresponding meanings. The dataset will include features extracted from hand gestures, such as hand shape, movement, and position. It should be noted that the accuracy of the recognition system depends on the quality of dataset, feature selection, and SVM parameters. Also, pre-processing steps such as hand segmentation and normalization may be necessary to improve performance. Present paper proposes static hand gesture recognition system for ArSL. Meanwhile, it uses multi-class support vector machine (MSVM) algorithm. The current study discovered a histogram of oriented gradients (HOG) from each sample image. In addition to performing principal component analysis (PCA) on HOG image samples with 100% accuracy. Test results on ArSL showed that this method is very effective and with high accuracy. Whereas, using the Z-score normalization method, the features and sigma belonging to one class became more closely related and separated from the other class.

Keywords: Hand Gesture Recognition (HGR), Machine Learning (ML), Arabic Sign Language (Arsl), Multiclass Support Vector Machine (MSVM), Histogram of Oriented Gradients (HOG), Principal Component Analysis (PCA).

DOI: 10.25673/120432

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References:

  1. V. Gajjar, V. Mavani, A. Gurnani, and Gajjar, "Hand gesture real time paint tool-box: Machine learning approach," 2017 IEEE international conference on power, control, signals and instrumentation engineering (ICPCSI). IEEE, 2017, pp. 856-860.
  2. M. E. Benalcázar, A. G. Jaramillo, A. Zea, and A. Páez, “Hand Gesture Recognition Using Machine Learning and the Myo Armband,” 2017 25th Eur. Signal Process. Conf. (EUSIPCO, pp. 1040–1044, [Online]. Available: doi:10.23919/eusipco.2017.8081366.
  3. B. Abhishek, K. Krishi, M. Meghana, M. Daaniyaal, and H. S. Anupama, “Hand gesture recognition using machine learning algorithms,” Comput. Sci. Inf. Technol., vol. 1, no. 3, pp. 1734–1737, 2020, doi: 10.11591/csit.v1i3.p116-120.
  4. S. C. Mesbahi, J. Riffi, and H. Tairi, “Hand gesture recognition based on convexity approach and background subtraction,” in 2018 IEEE, pp. 1–5.
  5. B. Yu, Z. Luo, H. Wu, and S. Li, “Hand gesture recognition based on attentive feature fusion,” no. May, pp. 1–9, 2020, doi: 10.1002/cpe.5910.
  6. R. Agrawal and N. Gupta, “Real Time Hand Gesture Recognition for Human Computer Interaction,” 2016 IEEE 6th Int. Conf. Adv. Comput., doi: 10.1109/IACC.2016.93.
  7. H. Saleh and I. Hussein, "Enabling smart mobility with connected and intelligent vehicles: The E-VANET framework," in Proc. Int. Conf. Appl. Innov. IT, vol. 12, no. 2, Anhalt University of Applied Sciences, 2024.
  8. G. Alani, A. Ali, G. Cosma, A. Taherkhani, and T. M. Mcginnity, "Hand gesture recognition using an adapted convolutional neural network with data augmentation," pp. 5-12, 2018, doi: 10.1109/INFOMAN.2018.8392660.
  9. M. HafizurRahman and J. Afrin, “Hand Gesture Recognition using Multiclass Support Vector Machine,” International Journal of Computer Applications, vol. 74, no. 1. pp. 39–43, 2013, doi: 10.5120/12852-9367.
  10. M. K. Ahuja and A. Singh, “Static vision based Hand Gesture recognition using principal component analysis,” Proc. 2015 IEEE 3rd Int. Conf. MOOCs, Innov. Technol. Educ. MITE 2015, pp. 402–406, 2016, doi: 10.1109/MITE.2015.7375353.
  11. P. Parvathy, K. Subramaniam, G. K. D. P. Venkatesan, P. Karthikaikumar, J. Varghese, and T. Jayasankar, “Development of hand gesture recognition system using machine learning,” J. Ambient Intell. Humaniz. Comput., 2020, doi: 10.1007/s12652-020-02314-2.
  12. A. Ghotkar, “Study of vision based Hand Gesture recognition using indian sign language,” no. International Journal on smart sensing and intelligent systems vol.7 no. 1, march 2014.
  13. C. Maharani, D. A., Fakhrurroja, H., Riyanto, and Machbub, “Hand Gesture Recognition Using K-Means Clustering and Support Vector Machine,” IEEE Symp. Comput. Appl. Ind. Electron. (ISCAIE)., 2018, [Online]. Available: doi:10.1109/iscaie.2018.8405435.
  14. P. Krömer, H. Zhang, Y. Liang, and J. S. Pan, Proceedings of the Fifth Euro-China Conference on Intelligent Data Analysis and Applications, vol. 891, 2018. Springer.
  15. F. Wahid, R. Tafreshi, M. Al-sowaidi, and R. Langari, “Subject-Independent Hand Gesture Recognition using Normalization and Machine Learning Algorithms,” J. Comput. Sci., 2018, doi: 10.1016/j.jocs.2018.04.019.
  16. M. Lorentzon, “Feature extraction for image selection using machine learning”, M.s.C Thesis in Electrical Engineering Department of Electrical Engineering, Linkoping University, 2017.
  17. H. S. Dadi and G. K. Mohan Pillutla, “Improved Face Recognition Rate Using HOG Features and SVM Classifier,” IOSR J. Electron. Commun. Eng., vol. 11, no.4, 2016.
  18. H. I. Mohammed, J. Waleed, and S. Albawi, “An Inclusive Survey of Machine Learning based Hand Gestures Recognition Systems in Recent Applications”, IOP Conf. Ser. Mater. Sci. Eng., vol. 1076, no. 1, p. 012047, 2021.
  19. E. P. Chou and T.-W. Ko, "Dimension Reduction of High-Dimensional Datasets Based on Stepwise SVM," arXiv preprint arXiv:1711.03346, pp. 1-18, Nov. 9, 2017.
  20. S. Veluchamy, L.R. Karlmarx, and J.J. Sudha, “Vision Based Gesturally Controllable Human Computer Interaction System,” in 2015 International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM), p. pp.8-15.
  21. S. S. Mohammed and J. M. Al-Tuwaijari, “Skin Disease Classification System Based on Machine Learning Technique: A Survey,”, IOP Conf. Ser. Mater. Sci. Eng., vol. 1076, no. 1, p. 012045, 2021.
  22. F Cabitza, A Campagner, D Ferrari, C Di Resta, D Ceriotti, and E Sabetta,” Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests” Clinical Chemistry and Laboratory Medicine (CCLM), vol. 59, no. 2, pp. 421–431, 2021.
  23. I. Mishkhal, S. A. AL Kareem, H. H. Saleh, A. Alqayyar, I. Hussein and I. A. Jassim, "Solving Course Timetabling Problem Based on the Edge Coloring Methodology by Using Jedite," 2019 1st AL-Noor International Conference for Science and Technology (NICST), Sulimanyiah, Iraq, 2019, pp. 68-72, doi: 10.1109/NICST49484.2019.9043794.
  24. Q. Bani Baker, N. Alqudah, T. Alsmadi, and R. Awawdeh, “Image‐Based Arabic Sign Language Recognition System Using Transfer Deep Learning Models”, Applied Computational Intelligence and Soft Computing, 2023.
  25. B Hisham, ad A Hamouda, ‘Arabic sign language recognition using Ada-Boosting based on a leap motion controller.’ International Journal of Information Technology , vol. 13,pp. 1221-1234, 2021.
  26. M. A. Almasre, and H. Al-Nuaim, “A comparison of Arabic sign language dynamic gesture recognition models.” Heliyon, vol. 6, no. 3, March 2020.
  27. M. Halabi, and Y. Harkouss,” Real-time arabic sign language recognition system using sensory glove and machine learning”, Neural Computing and Applications, 2025.
  28. Nsaif, Wassem Saad, et al. "Conversational agents: An exploration into Chatbot evolution, architecture, and important techniques." The Eurasia Proceedings of Science, Technology, Engineering & Mathematics (EPSTEM) 27 (2024): 246-262.
  29. M. S. Amin, and S. T. H. Rizvi, “Sign gesture classification and recognition using machine learning”, Cybernetics and Systems, 2023.
  30. B. Hisham, and A. Hamouda, “Arabic Static and Dynamic Gestures Recognition Using Leap Motion.” J. Comput. Sci. vol. 13, no.8, pp. 337-354, 2017.
  31. M. M. Khattab, A. M. Zeki, S. S. Matter, M. A. Abdella, R. A. E. Atiia, and A. M. Soliman, "Alphabet Recognition in Arabic Sign Language: A Machine Learning Perspective," Journal of Qena Faculty of Arts, vol. 33, no. 62, pp. 1-32, Jan. 2024, [Online]. Available: https://journals.ekb.eg/article_348677.html, doi: 10.21608/qarts.2024.267418.1882.
  32. T. H. Noor, A. Noor, A. F. Alharbi, A. Faisal, R. Alrashidi, A. S. Alsaedi, G. Alharbi, T. Alsanoosy, and A. Alsaeedi, ” Real-time arabic sign language recognition using a hybrid deep learning model”, Sensors, 2024.
  33. S. Aly, and W. Aly, “DeepArSLR: A novel signer-independent deep learning framework for isolated arabic sign language gestures recognition”, IEEE Access, pp. 83199-83212, 2020.
  34. M. A. Almasre, and H. Al-Nuaim, “Recognizing Arabic Sign Language gestures using depth sensors and a KSVM classifier”, 2016 8th Computer Science and Electronic Engineering (CEEC), 2016.
  35. M. A. Ali, M. R. Ewis, G. E. Mohamed, H. H. Ali, and H. M. Moftah, “Arabic sign language recognition (ArSL) approach using support vector machine”, 2017 27th International Conference on Computer Theory and Applications (ICCTA). IEEE, 2017.
  36. A. B. H. Amor, O. El. Ghoul, and M. Jemni, “A deep learning based approach for Arabic Sign language alphabet recognition using electromyographic signals”, 2021 8th International Conference on ICT & Accessibility (ICTA), 2021.
  37. R. E. Rwelli, O. R. Shahin, and A, I. Taloba, “Gesture based Arabic sign language recognition for impaired people based on convolution neural network”, arXiv preprint arXiv:2203.05602, 2022.
  38. A. Hamed, N.A. Belal, K.M. Mahar,” Arabic sign language alphabet recognition based on HOG-PCA using microsoft kinect in complex backgrounds”, 2016 IEEE 6th international conference on advanced computing (IACC), 2016.
  39. R.M. Duwairi, and Z.A. Halloush, “Automatic recognition of Arabic alphabets sign language using deep learning.”, International Journal of Electrical & Computer Engineering vol. 12, no. 3, pp. 2996
  40. E. Soares and P. Angelov, “A large dataset of real patients CT scans for COVID-19 identification,” Harv. Dataverse, vol. 1, pp. 1–8, 2020.
  41. A. K. Dutta, N. A. Aljarallah, T. Abirami, M. Sundarrajan, S. Kadry, Y. Nam, and C. W. Jeong, “Optimal deep‐learning‐enabled intelligent decision support system for SARS‐CoV‐2 classification”, Journal of Healthcare Engineering, 2022.
  42. P. T. Hai, H. C. Thinh, B. V. an Phuc, and H. H. Kha,” Automatic feature extraction for Vietnamese sign language recognition using support vector machine”, 2018 2nd International Conference on Recent Advances in Signal Processing, Telecommunications & Computing (SigTelCom). IEEE, 2018.
  43. J.L. Raheja, A. Mishra, and A. Chaudhary,” Indian sign language recognition using SVM”, Pattern Recognition and Image Analysis, vol. 26,pp. 434-441,‏2016.
  44. M. A. Uddin, and S. A. Chowdhury, “Hand sign language recognition for bangla alphabet using support vector machine”, 2016 International Conference on Innovations in Science, Engineering and Technology (ICISET). IEEE, 2016.‏
  45. A. Novianty, and F. Azmi, “Sign Language Recognition using Principal Component Analysis and Support Vector Machine”, IJAIT (International Journal of Applied Information Technology, vol. 04, no. 01, 2020.
  46. I. Mishkhal, S. A. A. L. Kareem, H. H. Saleh, A. Alqayyar, I. Hussein, and I. A. Jassim, "Solving Course Timetabling Problem Based on the Edge Coloring Methodology by Using Jedite," in 2019 1st AL-Noor International Conference for Science and Technology (NICST), 2019.


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