Abstract
The literature contains many proposed solutions for automatic sign language recognition. However, the ArSL (Arabic Sign Language), unlike ASL (American Sign Language), did not take much attention from the research community. In this paper, we propose a new system which does not require a deaf wear inconvenient devices like gloves to simplify the process of hand recognition. The system is based on gesture extracted from 2D images. The Scale Invariant Features Transform (SIFT) technique is used to achieve this task as it extracts invariant features which are robust to rotation and occlusion. Also, the Linear Discriminant Analysis (LDA) technique is used to solve dimensionality problem of the extracted feature vectors and to increase the separability between classes, thus increasing the accuracy of the introduced system. The classifiers, Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), and minimum distance will be used to identify the Arabic sign characters. Experiments are conducted to check the performance of the proposed system and it showed that the accuracy of the obtained results is around 99%. Also, the experiments proved that the proposed system is robust against any rotation and they achieved an identification rate near to 99%. Moreover, the evaluation shown that the system is comparable to the related work.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Assaleh, K., Al-Rousan, M.: Recognition of arabic sign language alphabet using polynomial classifiers. EURASIP Journal on Applied Signal Processing 2005, 2136–2145 (2005)
Samir, A., Aboul-Ela, M.: Error detection and correction approach for arabic sign language recognition. In: 2012 Seventh International Conference on Computer Engineering & Systems (ICCES), pp. 117–123. IEEE (2012)
Tolba, M., Elons, A.: Recent developments in sign language recognition systems. In: 2013 8th International Conference on Computer Engineering & Systems (ICCES), pp. xxxvi–xlii. IEEE (2013)
El-Bendary, N., Zawbaa, H.M., Daoud, M.S., Nakamatsu, K., et al.: Arslat: Arabic sign language alphabets translator. In: 2010 International Conference on Computer Information Systems and Industrial Management Applications (CISIM), pp. 590–595. IEEE (2010)
Tolba, M., Samir, A., Aboul-Ela, M.: Arabic sign language continuous sentences recognition using pcnn and graph matching. Neural Computing and Applications 23(3-4), 999–1010 (2013)
Al-Jarrah, O., Halawani, A.: Recognition of gestures in arabic sign language using neuro-fuzzy systems. Artificial Intelligence 133(1), 117–138 (2001)
Albelwi, N.R., Alginahi, Y.M.: Real-time arabic sign language (arsl) recognition. In: International Conference on Communications and Information Technology (ICCIT 2012), Tunisia, pp. 497–501 (2012)
Tolba, M., Samir, A., Abul-Ela, M.: A proposed graph matching technique for arabic sign language continuous sentences recognition. In: 8th IEEE International Conference on Informatics and Systems (INFOS), pp. 14–20 (2012)
Al-Jarrah, O., Al-Omari, F.A.: Improving gesture recognition in the arabic sign language using texture analysis. Journal of Applied Artificial Intelligence 21(1), 11–33 (2007)
Youssif, A.A., Aboutabl, A.E., Ali, H.H.: Arabic sign language (arsl) recognition system using hmm. International Journal of Advanced Computer Science and Applications (IJACSA) 2(11) (2011)
Mohandes, M., Deriche, M., Liu, J.: Image-based and sensor-based approaches to arabic sign language recognition. IEEE Transactions on Human-Machine Systems 44(4), 551–557 (2014)
Mohandes, M., Deriche, M.: Image based arabic sign language recognition. In: Proceedings IEEE of the Eighth International Symposium on Signal Processing and Its Applications, vol. 1, pp. 86–89. IEEE (2005)
El-Gayyar, M., Yamany, H.F.E., Gaber, T., Hassanien, A.E.: Social network framework for deaf and blind people based on cloud computing. In: Ganzha, M., Maciaszek, L.A., Paprzycki, M. (eds.) FedCSIS, pp. 1301–1307 (2013)
Meng, Y., Tiddeman, B., et al.: Implementing the scale invariant feature transform (sift) method. Citeseer (2008), http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.102.180
Lowe, D.G.: Object recognition from local scale-invariant features. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 1150–1157. IEEE (1999)
Cheung, W., Hamarneh, G.: n-sift: n-dimensional scale invariant feature transform. IEEE Transactions on Image Processing 18(9), 2012–2021 (2009)
Scholkopft, B., Mullert, K.R.: Fisher discriminant analysis with kernels. In: Proceedings of the IEEE Signal Processing Society Workshops, Neural Networks for Signal Processing IX, August 23-25, pp. 41–48 (1999)
Elhariri, E., El-Bendary, N., Fouad, M.M.M., Platoš, J., Hassanien, A.E., Hussein, A.M.M.: Multi-class SVM based classification approach for tomato ripeness. In: Abraham, A., Krömer, P., Snášel, V. (eds.) Innovations in Bio-inspired Computing and Applications. AISC, vol. 237, pp. 175–186. Springer, Heidelberg (2014)
Lee, Y.: Handwritten digit recognition using k nearest-neighbor, radial-basis function, and back propagation neural networks. Neural Computation 3(3), 440–449 (1991)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Tharwat, A., Gaber, T., Hassanien, A.E., Shahin, M.K., Refaat, B. (2015). SIFT-Based Arabic Sign Language Recognition System. In: Abraham, A., Krömer, P., Snasel, V. (eds) Afro-European Conference for Industrial Advancement. Advances in Intelligent Systems and Computing, vol 334. Springer, Cham. https://doi.org/10.1007/978-3-319-13572-4_30
Download citation
DOI: https://doi.org/10.1007/978-3-319-13572-4_30
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13571-7
Online ISBN: 978-3-319-13572-4
eBook Packages: EngineeringEngineering (R0)