A Support System for Speech Impaired People using the Indian Sign Language

A Support System for Speech Impaired People using the Indian Sign Language

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2017 by IJETT Journal
Volume-46 Number-7
Year of Publication : 2017
Authors : Pushpendra Kumar Tiwari, Sithara Kamalakkannan, S.V. Karthigaipriya, Logeshwari R
DOI :  10.14445/22315381/IJETT-V46P261

Citation 

Pushpendra Kumar Tiwari, Sithara Kamalakkannan, S.V. Karthigaipriya, Logeshwari R "A Support System for Speech Impaired People using the Indian Sign Language", International Journal of Engineering Trends and Technology (IJETT), V46(7),363-366 April 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract
Sign Language Recognition is a rapidly growing field of research. Several techniques have been developed recently. In this paper, we propose a system that uses Support Vector Machine (SVM) with image feature extractionas a classification technique for the recognition of the Indian Sign Language. The system comprises of four parts: Image capture,Background Subtraction, Feature Extraction and Classification. 26 signs were considered in this paper, each having over 200 samples to train the data. An accuracy of 98% was achieved during testing.

 References

[1] A. S. Ghotkar, R. Khatal, S. Khupase, S. Asati, and M. Hadap, “Hand Gesture Recognition for Indian Sign Language”, IEEE International Conference on Computer Communication and Informatics (ICCCI), Jan. 10-12, 2012, Coimbatore, India.
[2] D.Karthikeyan, Mrs.G.Muthulakshmi., “English Letters Finger Spelling Sign Language Recognition System”, International Journal of Engineering Trends and Technology (IJETT) – Volume 10 Number 7 - Apr 2014
[3] D. Y. Huang, W. C. Hu, and S. H. Chang, “Vision-based Hand Gesture Recognition Using PCA+Gabor Filters and svm”, ieee Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2009, pp. 1-4.
[4] Y. Fang, K. Wang, J. Cheng, and H. Lu, “A Real-Time Hand Gesture Recognition Method”, IEEE ICME, 2007, pp. 995-998.
[5] 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.
[6] J. Weissmann and R. Salomon, “Gesture Recognition for Virtual Reality Applications Using Data Gloves and Neural Networks”, IEEE, 1999, pp. 2043-2046.
[7] 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.
[8] Kumara Maruthi M. Mullur, “Indian Sign Language Recognition System”, International Journal of Engineering Trends and Technology (IJETT) – Volume 21 Number 9 – March 2015
[9] L. K. Lee, S. Y. An, and S. Y. Oh, “Robust Fingertip Extraction with Improved Skin Color Segmentation for Finger Gesture Recognition in Human-Robot Interaction”, WCCI 2012 IEEE World Congress on Computational Intelligence, June, 10-15, 2012, Brisbane, Australia.
[10] M. V. Lamar, S. Bhuiyan, and A. Iwata, “Hand Alphabet Recognition Using Morphological PCA and Neural Networks”, IEEE, 1999, pp. 2839-2844.
[11] Manigandan M. and I. M Jackin, “Wireless Vision based Mobile Robot control using Hand Gesture Recognition through Perceptual Color Space”, IEEE International Conference on Advances in Computer Engineering, 2010, pp. 95-99.
[12] O. B. Henia and S. Bouakaz, “3D Hand Model Animation with a New Data-Driven Method”,Workshop on Digital Media and Digital Content Management (IEEE Computer Society), 2011,pp. 72-76.
[13] Cheok Ming Jin, Zaid Omar, Mohamed HishamJaward, “A Mobile Application of American Sign Language Translation via Image Processing Algorithms”, 2016 IEEE Region 10 Symposium (TENSYMP), Bali, Indonesia
[14] R. Gopalan and B. Dariush, “Towards a Vision Based Hand Gesture Interface for RoboticGrasping”, The ieee/rsj International Conference on Intelligent Robots and Systems, October 11-15, 2009, St. Louis, usa, pp. 1452-1459.
[15] S. Saengsri, V. Niennattrakul, and C.A. Ratanamahatana, “TFRS: Thai Finger-Spelling Sign Language Recognition System”, IEEE, 2012, pp. 457-462.
[16] S. K. Yewale and P. K. Bharne, “Hand Gesture Recognition Using Different Algorithms Basedon Artificial Neural Network”, IEEE, 2011, pp. 287-292.
[17] 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.
[18] Dr. P.M. Mahajan, Jagruti S. Chaudhari, “Review of Finger Spelling Sign Language Recognition”, International Journal of Engineering Trends and Technology (IJETT) – Volume 22 Number 9-April 2015.

Keywords
Indian sign language, Support Vector Machine, feature extraction, image classification.