Google scholar arxiv informatics ads IJAIS publications are indexed with Google Scholar, NASA ADS, Informatics et. al.

Call for Paper


January Edition 2023

International Journal of Applied Information Systems solicits high quality original research papers for the January 2023 Edition of the journal. The last date of research paper submission is December 15, 2022.

Design of ANFIS System for Recognition of Single Hand and Two Hand Signs for Indian Sign Language

Shweta Dour, J. M. Kundargi Published in Pattern Recognition

IJAIS Proceedings on International Conference and workshop on Advanced Computing 2013
Year of Publication: 2013
© 2012 by IJAIS Journal
Download full text
  1. Shweta Dour and J M Kundargi. Article: Design of ANFIS System for Recognition of Single Hand and Two Hand Signs for Indian Sign Language. IJAIS Proceedings on International Conference and workshop on Advanced Computing 2013 ICWAC(2):18-25, June 2013. BibTeX

    	author = "Shweta Dour and J. M. Kundargi",
    	title = "Article: Design of ANFIS System for Recognition of Single Hand and Two Hand Signs for Indian Sign Language",
    	journal = "IJAIS Proceedings on International Conference and workshop on Advanced Computing 2013",
    	year = 2013,
    	volume = "ICWAC",
    	number = 2,
    	pages = "18-25",
    	month = "June",
    	note = "Published by Foundation of Computer Science, New York, USA"


Sign language develops independently from the spoken language of the region . The sign language used in India is commonly known as Indian Sign Language (ISL). A functioning sign language recognition system can provide an opportunity for a deaf/mute person to communicate with non-signing people without the need for an interpreter. Our system deals with images of bare hands, which allows the user to interact with the system in a natural way. In doing so, we have designed a collection of ANFIS networks, each of which is trained to recognize one sign gesture. Features of the input gesture of the sign are extracted obtaining feature vector. The recognition algorithm translates each quantitative value of the feature into fuzzy sets of linguistic terms using membership functions. The membership functions are formed by the fuzzy partitioning of the feature space into fuzzy equivalence classes, using the feature cluster centers generated by the subtractive clustering technique. The subtractive clustering algorithm and the least-squares estimator are used to identify the fuzzy inference system, and the training is achieved using the hybrid learning algorithm.


  1. Vasishta M. , Woodward J. , DeSantis S. 1998, "An Introduction to Indian Sign Language", All India Federation of the Deaf (Third Edition).
  2. Tirthankar Dasgupta, Sambit Shukla, Sandeep Kumar,Synny Diwakar, Anupam Basu,"A Multilingual Multimedia Indian Sign Language Dictionary Tool", The 6'th Workshop on Asian Language Resources, pp. 57-64, 2008.
  3. Anup Nandy, Jay Shankar Prasad, Pavan Chakraborty, G. C. Nandi, Soumik Mondal, "Classification of Indian Sign Language In Real Time", In the proceedings of International Journal on Computer Engineering and Information Technology (IJCEIT), Vol. 10, No. 15,pp. 52-57, Feb. 2010.
  4. Indian Sign language, empowering the deaf, Indian Sign Language Database.
  5. Binh, N. D . . T. Ejima, "Hand gesture recognition using fuzzy neural network," in Proc. of ICGST Int. Conf. on Graphics, Vision and Image Processing, pp. 1-6,Cairo, Egypt,2005.
  6. Mu-Chun Su, A Fuzzy Rule-Based Approach to Spatio-Temporal Hand Gesture Recognition, IEEE Transactions on systems, man and Cybernetics-Part C Applications and Reviews, VOL. 30, NO. 2, MAY 2000
  7. Jong-Sung Kim, Won Jang, and Zeungnam Bien A Dynamic Gesture Recognition System for the Korean Sign Language (KSL) IEEE Transactions On Systems, Man, And Cybernetics-Part B: Cybernetics, Vol. 26, No. 2, April 1996
  8. Balazs Tusor ,Annamaria R. Varkonyi-K6czy, Circular Fuzzy Neural Network Based Hand Gesture and Posture Modeling IEEE Transactions On Instrumentation And Measurement, Vol. 60, No. 5, May 2011
  9. Omar Al-Jarrah , Alaa Halawani, Recognition of gestures in Arabic sign language using neuro-fuzzy systems Artificial Intelligence 133 (2001) 117–138
  10. Sugeno, M. ?Industrial applications of fuzzy control?, Elsevier Science Pub. Co, (1985). (18)
  11. Takagi T, Sugeno M, 1985. Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, 15: 116–132.
  12. Sin, S. K. , and De Figueiredo. 1993. Fuzzy System Designing Through Fuzzy Clustering and Optimal preDefuzzification. Proc. IEEE International Conference on Fuzzy Systems. 190-195.
  13. Gomez, A. F. , M. Delgado, and M. A. Vila. 1999. About the Use of Fuzzy Clustering Techniques for Fuzzy Model Identification. Fuzzy Set and Systems. 106: 179-188.
  14. Demirli, K. , S. X. Cheng, and P. Muthukumaran. 2003. Subtractive Clustering Based Modeling of Job Sequencing with Parametric Search. Fuzzy Sets and Systems. 137: 235-270.
  15. Surmann, H. , and A. Selenschtschikow. 2002. Automatic Generation of Fuzzy Rule Bases: Example I. Proc. of the NF2002 First International ICSC on Neuro-Fuzzy Technologies. January 2002.
  16. S. L. Chiu, Fuzzy model identification based on cluster estimation, J. Intelligent and Fuzzy Systems 2 (3) (1994) 267–278.
  17. A. Khotanzad, J. -H. Lu, Classification of invariant image representations using a neural network, IEEE Transactions Acoustics, Speech, and Signal Processing 38 (1990) 1028–1038.


Indian Sign Language (ISL), Adaptive Neuro Fuzzy Inference System(ANFIS) , Subtractive Clustering