Skip to main content
SpringerLink
Log in

Recognition of Amharic sign language with Amharic alphabet signs using ANN and SVM

  • Original article
  • Published:
The Visual Computer Aims and scope Submit manuscript

Abstract

Sign language is a natural language mostly used by persons with hearing- and speech-based impairments to communicate with other people. In these modern times, sign language guides are used to eliminate the communication gap between people having hearing impairments and those with or without hearing impairments; however, they are very limited in number. To solve this challenge, automatic sign language recognition systems are developed to better reduce the communication gap for people with hearing disabilities. This paper presents the development of an automatic Amharic sign language translator which translates Amharic alphabet signs into their corresponding text using digital image processing and machine learning algorithms. The proposed system has four major developmental stages which include preprocessing, segmentation, feature extraction and classification. A total number of thirty-four features were extracted from shape, motion and color of hand gestures to represent both the base and derived class of Amharic sign characters. Classification models were built using artificial neural network (ANN) and multi-class support vector machine (SVM). The results show that the recognition system is capable of recognizing the Amharic alphabet signs with an average accuracy of 80.82% and 98.06% using the ANN and SVM classifiers, respectively.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Data availability

The authors declare that all data presented in this work was generated during the course of the work and any other source has been appropriately reference within the manuscript.

References

  1. Choudhury, N.N., Kayas, G.: Automatic recognition of Bangla sign language. Bachelor's Project, Department of Computer Science and Engineering, BRAC University, Knoxville—USA (2012)

  2. Belay, B., Habtegebrial, T., Meshesha, M., Liwicki, M., Belay, G., Stricker, D.: Amharic OCR: an end-to-end learning. Appl. Sci. 10(3), 1–13 (2020). https://doi.org/10.3390/app10031117

    Article  Google Scholar 

  3. Ethiopian National Association for Deaf. BERTAT, Yearly Magazine, Ethiopia (1997)

  4. Kumar, B.P.P., Manjunatha, M.B.: Performance analysis of KNN, SVM and ANN techniques for gesture recognition system. Indian J. Sci. Technol. 9(1), 1–8 (2016). https://doi.org/10.17485/ijst/2017/v9iS1/111145

    Article  Google Scholar 

  5. Ekbote, J., Joshi, M.: Indian sign language recognition using ANN and SVM classifiers. In: International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS) (2017). https://doi.org/10.1109/iciiecs.2017.8276111

  6. Chen, Y., Zhang, W.: Research and implementation of sign language recognition method based on Kinect. In: 2nd IEEE International Conference on Computer and Communications (ICCC) (2016). https://doi.org/10.1109/compcomm.2016.7925041

  7. Nagarajan, S., Subashini, T.S., Balasubramanian, M.: Visual interpretation of ASL finger spelling using hough transform and support vector machine. Int. J. Adv. Res. Comput. Commun. Eng. 4(6), 28–39 (2015)

    Google Scholar 

  8. Tavari, V., Deorankar, I.: Implementation of neural network based hand gesture recognition system. Int. J. Eng. Comput. Sci. 3(6), 364–386 (2014)

    Google Scholar 

  9. Quan, Y., Jinye, P.: Chinese sign language recognition for a vision-based multi-features classifier. In: International Symposium on Computer Science and Computational Technology, Shaanxi Xi‟an, China (2008). https://doi.org/10.1109/ISCSCT.2008.374

  10. Admasu, Y.F., Raimond, K.: Ethiopian sign language recognition using Artificial Neural Network. In: 10th IEEE International Conference on Intelligent Systems Design and Applications, pp. 995–1000 (2010). https://doi.org/10.1109/isda.2010.5687057

  11. Gimbi, T.: Recognition of isolated signs in Ethiopian sign language. Master's thesis, Addis Ababa University (2014)

  12. Tesfaye, M.: Machine translation approach to translate Amharic text to Ethiopian sign language. In: 2nd Proceedings of IEEE International Conference, pp. 252–273 (2010)

  13. Zerubabel, L.: Ethiopian finger spelling classification: a study to automate Ethiopian sign language. Master's thesis, Addis Ababa University, Addis Ababa, Ethiopia (2008)

  14. Tsegaye, A.: Offline candidate hand gesture selection and trajectory determination for continuous Ethiopian sign language. Master's thesis, Addis Ababa University, Addis Ababa, Ethiopia (2011)

  15. Pugeault, N., Bowden, R.: Spelling it out: real-time ASL fingerspelling recognition. In: IEEE Work-Shop on Consumer Depth Cameras for Computer Vision, Barcelona, Spain (2011)

  16. Ricco, S., Tomasi, C.: Finger spelling recognition through classification of letter-to-letter transitions. Proc. ACC 3, 214–225 (2010)

    Google Scholar 

  17. Singha, J.: Indian sign language recognition using Eigen value weighted Euclidean distance based classification technique 4(2) (2013)

  18. Salau, A.O., Jain, S.: Feature extraction: a survey of the types, techniques, and applications. In: 5th IEEE International Conference on Signal Processing and Communication (ICSC), Noida, India, pp. 158–164 (2019). https://doi.org/10.1109/ICSC45622.2019.8938371

  19. Chimdi, W.: Ethiopian sign language and educational accessibility for the deaf community: a case study on Jimma, Nekemte, Addis Ababa and Hawasa towns. J. Lang. Cult. 6(2), 9–17 (2015). https://doi.org/10.5897/jlc2014.0298

    Article  Google Scholar 

  20. Rupe, J.: Vision-based hand shape identification for sign language recognition. Master's thesis, Rochester Institute of Technology (2005)

  21. Hammouda, G., Sellami, D., Hammouda, A.: Pattern recognition based on compound complex shape-invariant Radon transform. Vis. Comput. 36, 279–290 (2020). https://doi.org/10.1007/s00371-018-1604-9

    Article  Google Scholar 

  22. Sharma, S., Saxena, V.P., Satish, K.: Comparative analysis on sign language recognition system. Int. J. Sci. Technol. Res. 8(8), 981–990 (2019)

    Google Scholar 

  23. Yusnita, L., Hadisukmana, N., Wahyu, R. B., Roestam, R., Wahyu, Y.: Implementation of real-time static hand gesture recognition using artificial neural network. In: 4th International Conference on Computer Applications and Information Processing Technology (CAIPT) (2017). https://doi.org/10.1109/caipt.2017.8320692

Download references

Funding

The authors declare no funding for this research.

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Debre Markos University, Debre Markos, Ethiopia

    Nigus Kefyalew Tamiru

  2. Department of Electrical and Computer Engineering, Addis Ababa University, Addis Ababa, Ethiopia

    Menore Tekeba

  3. Department of Electrical/Electronics and Computer Engineering, Afe Babalola University, Ado-Ekiti, Nigeria

    Ayodeji Olalekan Salau

Authors
  1. Nigus Kefyalew Tamiru
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Menore Tekeba
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ayodeji Olalekan Salau
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ayodeji Olalekan Salau.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest of any sort on this research.

Ethical approval

Authors affirm that the submitted material is ours and original.

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

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tamiru, N.K., Tekeba, M. & Salau, A.O. Recognition of Amharic sign language with Amharic alphabet signs using ANN and SVM. Vis Comput 38, 1703–1718 (2022). https://doi.org/10.1007/s00371-021-02099-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00371-021-02099-1

Keywords

Search

Navigation