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

Pushpendra Kumar Tiwari; Sithara Kamalakkannan; S.V. Karthigaipriya; Logeshwari R

doi:https://doi.org/10.14445/22315381/IJETT-V46P261

Research Article | Open Access | Download PDF

Volume 46 | Number 3 | Year 2017 | Article Id. IJETT-V46P261 | DOI : https://doi.org/10.14445/22315381/IJETT-V46P261

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

Pushpendra Kumar Tiwari, Sithara Kamalakkannan, S.V. Karthigaipriya, Logeshwari R

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), vol. 46, no. 3, pp. 363-366, 2017. Crossref, https://doi.org/10.14445/22315381/IJETT-V46P261

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.

Keywords

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

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.