Kurdish Sign Language Recognition System

Authors

  • Abdulla D. Hashim Department of Computer Science and Engineering, University of Kurdistan Hewler, Erbil, Kurdistan Region – F.R. Iraq
  • Fattah Alizadeh Department of Computer Science and Engineering, University of Kurdistan Hewler, Erbil, Kurdistan Region – F.R. Iraq http://orcid.org/0000-0002-0690-1147

DOI:

https://doi.org/10.25079/ukhjse.v2n1y2018.pp1-6

Keywords:

Image Processing, Kurdish, Machine Learning, Sign Language

Abstract

Deaf people all around the world face difficulty to communicate with the others. Hence, they use their own language to communicate with each other. On the other hand, it is difficult for deaf people to get used to technological services such as websites, television, mobile applications, and so on. This project aims to design a prototype system for deaf people to help them to communicate with other people and computers without relying on human interpreters. The proposed system is for letter-based Kurdish Sign Language (KuSL) which has not been introduced before. The system would be a real-time system that takes actions immediately after detecting hand gestures. Three algorithms for detecting KuSL have been implemented and tested, two of them are well-known methods that have been implemented and tested by other researchers, and the third one has been introduced in this paper for the 1st time. The new algorithm is named Gridbased gesture descriptor. It turned out to be the best method for the recognition of Kurdish hand signs. Furthermore, the result of the algorithm was 67% accuracy of detecting hand gestures. Finally, the other well-known algorithms are named scale invariant feature transform and speeded-up robust features, and they responded with 42% of accuracy.

Downloads

Download data is not yet available.

Author Biographies

  • Abdulla D. Hashim, Department of Computer Science and Engineering, University of Kurdistan Hewler, Erbil, Kurdistan Region – F.R. Iraq

    Abdullah Dlshad Hashim, was born in Erbil in 1992. He has received his B.Sc. degree from the department of Computer Science and Engineering of the University of Kurdistan Hewler in 2017. His research interests mainly comprises Image processing, Software Development and Evaluation.   

  • Fattah Alizadeh, Department of Computer Science and Engineering, University of Kurdistan Hewler, Erbil, Kurdistan Region – F.R. Iraq

    Fattah Alizadeh is Assistant Professor in Computer Science at the School of Computer Science and Engineering, University of Kurdistan Hewler (Kurdistan region, Iraq). He holds a Ph.D. degree in Computer Image Processing from the university of Dublin City University (Ireland), a Master-Degree in Computer Software Engineering from Iran University of Science and Technology, Tehran (Iran) and a Bachelor of Science from Shiraz University, Shiraz (Iran) in Computer System Engineering. His research interests include: Machine Vision, 3D Model Search, Retrieval and Segmentation, Machine Learning. 

References

Abdel-Fattah, M. A. (2005). Arabic sign language: A perspective. Journal of Deaf Studies and Deaf Education, 10(2), 212-221.

AI, S. (2017). SIFT: Theory and Practice. Available from: http://www.aishack.in/tutorials/sift-scale-invariant-feature-transformintroduction/. [Last accessed on 2017 Jun 19].

Albiol, A., Torres, L. & Del, E. J. (2001). Optimum Color Spaces for Skin Detection. 2001 International Conference, 1, 122-124.

Anon. (2015). Sign Language and Hearing Aids. Available from: http://www.disabled-world.com/disability/types/hearing/communication. [Last accessed on 2017 Jan 08].

Bay, H., Ess, A., Tuytelaars, T. & Van Gool, L. (2008). Speeded-up robust features (SURF). Computer Vision and Image Understanding, 110(3), 346-359.

Colender, M., Lepetit, V., Strech, C. & Fua, P. (2010). Brief: Binary robust independent elementary features. Computer Vision–ECCV 6314, 778-792.

Deng, G. & Cahill, L. (n.d.). An adaptive Gaussian Filter for Noise Reduction and Edge Detection. 1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference.

Eddy, S. R. (1996). Hidden Markov Models. Current Opinion in Structural Biology, 6(3), 361-365.

Haralic, R. M. & Shapiro, L. G. (1985). Image segmentation techniques. Computer Vision, Graphics, and Image Processing, 29(1), 100-132.

Karami, A., Zanj, B. & Sarkaleh, A.K. (2011). Persian sign language (PSL) recognition using wavelet transform and neural network. Expert Systems with Applications, 38(3), 2661-2667.

Lowe, D. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91-110.

Pham, M. T. & Cham, T.J. (2007), Fast Training and Selection of Haar Features using Statistics in Boosting-Based Face Detection. In: Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on. p. 1-7.

Reed, T. R. & Du Buf, J. M. H. (1993). A review of recent texture segmentation and feature extraction techniques. CVGIP: Image Understanding, 57(3), 359-372.

Satyanarayanan, M. (2001). Pervasive computing: Vision and challenges. IEEE Personal Communications, 8(4), 10-17.
Stemper, J. (n.d.). MotionSavvy UNI: 1st Sign Language to Voice System.

Available from: https://www.indiegogo.com/projects/motionsavvyuni-1st-sign-language-to-voice-system#. [Last accessed on 2017 Jan 21].

Truong, V. N., Yang, C. K. & Tran, Q. V. (2016). A Translator for American Sign Language to Text and Speech.
Wanshel, E. (2016). Students Invented Gloves That Can Translate Sign Language into Speech and Text. Available from: http://www.huffingtonpost.com/entry/navid-azodi-and-thomas-pryorsignaloud-gloves-translate-american-sign-language-into-speechtext_us_571fb38ae4b0f309baeee06d. [Last accessed on2017 Jan 12].

Weaver, J. & Pentland, A. (1998). Real-time American sign language recognition using desk and wearable computer based video. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20, 1371-1375.
UKHJSE Volume 2 Number 1 (2018)

Downloads

Published

2018-06-30

Issue

Vol. 2 No. 1 (2018): ISSUE TWO

Section

Research Articles

License

Authors who publish with this journal agree to the following terms:
1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License [CC BY-NC-ND 4.0] that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).

How to Cite

Kurdish Sign Language Recognition System. (2018). UKH Journal of Science and Engineering, 2(1), 1-6. https://doi.org/10.25079/ukhjse.v2n1y2018.pp1-6