English Letters Finger Spelling Sign Language Recognition System

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2014 by IJETT Journal
Volume-10 Number-7
Year of Publication : 2014
Authors : D.Karthikeyan , Mrs.G.Muthulakshmi


D.Karthikeyan , Mrs.G.Muthulakshmi. "English Letters Finger Spelling Sign Language Recognition System", International Journal of Engineering Trends and Technology (IJETT), V10(7),334-339 April 2014. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group


In this paper “English Letters Finger Spelling Sign Language Recognition System” implemented an image processing technique and Neural Network, designed for recognizing the sign language for deaf-dump person. The main objective of this paper is to convert the Fingerspelling image English [26 letters] into English speech for deaf-dump person. It will recognize and identify the finger spelling images which are presented in the English speech. 20 highest DCT coefficients are extracted from each finger spelling and are given as input to SVM model. Support Vector Machine (SVM) is used to recognize the sign (finger spelling) and play and display corresponding English speech and letters. In SVM have three kernels types Polynomial, Gaussian and Sigmoidal it’s applied for Finger spelling recognition into speech.


1. Supawadee Saengsri, Vit Niennattrakul, and Chotirat Ann Ratanamahatana. ‘TFRS: Thai Finger-Spelling Sing Language Recognition System’, IEEE, pp: 457- 462, 2012.
2. Subha Rajam, P. and Balakrishnan, G.,‘Recognition of Tamil Sing Language Alphabet using Image Processing to aid Deaf-Dumb People’, International Conference on Communication Technology and System Design, Elsevier, pp: 861-868,2011.
3. Subha Rajam, P. and Balakrishnan, G., ‘Real Time Sign Language Recognition System to aid Deaf-dumb People’, IEEE, pp: 737-742,2011.
4. Chance, M. Glenn, Divya Mandloi, Kanthi Sarella, and Muhammed Lonon, (2005) ‘An Image Processing Technique for the Translation of ASL Finger-Spelling to Digital Audi or text’, NTID International Instructional Technology and Education of the Deaf Symposium, pp: 1-7.
5. Nicolas Pugeault, and Richard Bowden,‘Spelling It Out: Real-Time ASL Fingerspelling Recognition’, IEEE Workshop on Consumer Depth Cameras for Computer Vision, pp. 1-6, 2011.
6. Deepika Tewari, and Sanjay Kumar Srivastava, ‘A Visual Recognition of Static Hand Gestures in Indian Sign Language based on Kohonen Self-Organizing Map Algorithm’ IJEAT, pp. 165-170,2012.
7. Sakshi Goyal, Ishita Sharma, and Shanu Sharma, ‘ Sign Language Recognition System for Deaf and Dumb People’, International Journal of Engineering Research & Technology, pp. 382 – 387, 2013.
8. Pingale Prerna Rambhau, ‘Recognition of Two Hand Gestures of word in British Sign Language (BSL)’, International Journal of Scientific and Research publications, pp. 1-5, 2013.
9. Stephan Liwicki and Mark Everingham, ‘Automatic Recognition of Fingerspelled Words in British Sign Language’, IEEE workshop, pp.1-9, 2009.

Fingerspelling, SVM, DCT, SRM, Zigzag