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Reseach Article

Advanced Marathi Sign Language Recognition using Computer Vision

by Amitkumar Shinde, Ramesh Kagalkar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 118 - Number 13
Year of Publication: 2015
Authors: Amitkumar Shinde, Ramesh Kagalkar
10.5120/20802-3485

Amitkumar Shinde, Ramesh Kagalkar . Advanced Marathi Sign Language Recognition using Computer Vision. International Journal of Computer Applications. 118, 13 ( May 2015), 1-7. DOI=10.5120/20802-3485

@article{ 10.5120/20802-3485,
author = { Amitkumar Shinde, Ramesh Kagalkar },
title = { Advanced Marathi Sign Language Recognition using Computer Vision },
journal = { International Journal of Computer Applications },
issue_date = { May 2015 },
volume = { 118 },
number = { 13 },
month = { May },
year = { 2015 },
issn = { 0975-8887 },
pages = { 1-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume118/number13/20802-3485/ },
doi = { 10.5120/20802-3485 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:01:33.816660+05:30
%A Amitkumar Shinde
%A Ramesh Kagalkar
%T Advanced Marathi Sign Language Recognition using Computer Vision
%J International Journal of Computer Applications
%@ 0975-8887
%V 118
%N 13
%P 1-7
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Sign language is a natural language that uses different means of expression for communication in everyday life. As compare to other sign language ISL interpretation has got less attention by researcher. This paper presents an Automatic translation system for gesture of manual alphabets in Marathi sign language. It deals with images of bare hands, which allows the user to interact with the system in a natural way. System provides an opportunity for deaf persons to communicate with normal people without the need of an interpreter. We are going to build a systems and methods for the automatic recognition of Marathi sign language. The first step of this system is to create a database of Marathi Sign Language. Hand segmentation is the most crucial step in every hand gesture recognition system since if we get better segmented output, better recognition rates can be achieved. The proposed system also includes efficient and robust hand segmentation and tracking algorithm to achieve better recognition rates. A large set of samples has been used to recognize 43 isolated words from the Standard Marathi sign language. In proposed system, we intend to recognize some very basic elements of sign language and to translate them to text and vice versa in Marathi language.

References
  1. Jung-BaeKim,Kwang-Hyun Park,Won_Chul Bang and Z. Zenn Bien Div. OfEE,Dept of EECS,KAIST, Daejeon, Republic of Korea. Continuous Korean sign language recognition using gesture segmentation and HMM. IEEE-2010.
  2. Venkatraman. S and T. V. Padmavathi, " Speech For The Disabled" , Proceedings of the International MultiConference of Engineers and Computer Scientists 2009 Vol I IMECS 2009,March 18 - 20, 2009.
  3. Gaurav N. Pradhan, Chuanjun Li, Balakrishnan Prabhakaran, "Hand Gesture-based Computing for Hearing and Speech Impaired", IEEE Multimedia Magazine, Vol. 15, No. 2, pp. 20-27, April-June 2008.
  4. Aleemkhalid ,Ali M, M. Usman, S. Mumtaz, Yousuf "BolthayHaath – Paskistan sign Language Recgnition" CSIDC 2005.
  5. Kadous, Waleed "GRASP: Recognition of Australian sign language using Instrumented gloves", Australia, October 1995,pp. 1-2,4-8.
  6. D. E. Pearson and J. P. Sumner, "An experimental visual telephone system for the deaf," J . Roy. Television Society vol. 16, no. 2. pp. 6-10, 1976.
  7. Guitarte Perez, J. F. ; Frangi, A. F. ; Lleida Solano, E. ; Lukas, K. "Lip Reading for Robust Speech Recognition on Embedded Devices" Volume 1, March 18-23, 2005 PP473 – 476.
  8. DONPEARSON "Visual Communication Systems for the Deaf" IEEE transactions on communications, vol. com-29, no. 12, December 1981.
  9. T. Masuko, K. Tokuda, T. Kobayashi, and S. Imai, "Speech synthesis using HMMs with dynamic features," in Proc. ZCASSP-96, May 1996, pp. 389-392.
  10. SantoshKumar,S. A. ; Ramasubramanian, V. " Automatic Language Identification Using Ergodic HMM" Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP'05). IEEE International Conference Vol1,March18-23,2005Page(s):609-612.
  11. honggangwang, ming c. leu and cemiloz, "American Sign Language recognition using multidimensional Hidden Markov Models. Journal of information science and engineering 22, 1109-1123(2006) . Department of Industrial Engineering Purdue University.
  12. Development of a new "Sign Writer" program Daniel Thomas
  13. CULSHAW MURRY (1983) "It Will Soon Be Dark. The situation of disabled in India. Delhi lithouse publications.
  14. DeshmukhDilip (1994) the status of sign language in deaf education in India. Signpost. Newsletter of International Sign Language Association 7(1) 49-52.
  15. Starner, T. (1995). Visual recognition of American Sign Language using hidden Markov models. Master's thesis, Massachusetts Institutes of Technology.
  16. Duda, R. O. , & Hart, P. E. (1972). Use of Hough transformation to detect lines and curves in pictures. Communications of the ACM, 15, 11-15.
  17. KouroshKhoshelham. Extending the use of Hough Transform to detect 3D objects in laser range data. ISPRS Workshop on Laser Scanning 2007 and SilviLaser2007, Espoo, September 12- 14,2007,Finland.
  18. Ong, S. , and Ranganath, S. (2005) Automatic sign language analysis: a survey and the future beyond lexical meaning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(6).
  19. Symeoinidis, k. (2000). "Hand gesture recognition-using neural networks. " Master's thesis, University of Surrey.
  20. Vamplew,p. (1996). Recognition of sign language using neural networks PhD Thesis, Department of computer Science, and University of Tasmania.
  21. Watson, R. (1993). A survey of gesture recognition techniques. Technical report TCD-CS-93-11, Department of computer Science, Trinty College Dublin.
  22. C. W. ong and surendra and ranganath Automatic sign language analysis: A survey and future beyond lexial meaning sylvie IEEE transactions on pattern analysis and machine intelligence, Vol27,No. 6,June 2006
  23. S. Saengsri, V. Niennattrakul, and C. A. Ratanamahatana, "TFRS: Thai Finger-Spelling Sign Language Recognition System", IEEE, 2012, pp. 457-462.
  24. J. H. Kim, N. D. Thang, and T. S. Kim, "3-D Hand Motion Tracking and Gesture Recognition Using a Data Glove", IEEE International Symposium on Industrial Electronics (ISIE), July 5-8, 2009, Seoul Olympic Parktel, Seoul , Korea, pp. 1013-1018.
  25. J. Weissmann and R. Salomon, "Gesture Recognition for Virtual Reality Applications Using Data Gloves and Neural Networks", IEEE, 1999, pp. 2043-2046
  26. M. V. Lamar, S. Bhuiyan, and A. Iwata, "Hand Alphabet Recognition Using Morphological PCA and Neural Networks", IEEE, 1999, pp. 2839-2844.
  27. T. Kapuscinski and M. Wysocki, "Hand Gesture Recognition for Man-Machine interaction", Second Workshop on Robot Motion and Control, October 18-20, 2001, pp. 91-96.
  28. M. Pahlevanzadeh, M. Vafadoost, and M. Shahnazi, "Sign Language Recognition", IEEE, 2007 .
  29. J. Rekha, J. Bhattacharya, and S. Majumder, "Shape, Texture and Local Movement Hand Gesture Features for Indian Sign Language Recognition", IEEE, 2011, pp. 30-35.
  30. Y. Wu and T. S. Huang, "Vision-based gesture recognition: A review," Lecture Notes Comput. Sci. , vol. 1739, pp. 103–115, 1999.
Index Terms

Computer Science
Information Sciences

Keywords

Marathi sign language Marathi alphabets Hand gesture Web-camera HSV image colour based hand extraction centre of gravity.