Weighted Euclidean Distance Based Sign Language Recognition Using Shape Features

  • S. Nagarajan
  • T. S. Subashini
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 325)


This paper proposes a real-time static hand gesture recognition system for American Sign Language alphabets. The input hand gestures from a simple background are captured by a camera and an image database is created. The proposed system consists of four stages namely preprocessing, segmentation, feature extraction, and classification. In the training phase, the hand region is detected and segmented from the gesture database images and various shape-based features such as area, perimeter, and roundness are extracted. The extracted features form a unique feature vector for a particular gesture. In the testing phase, the feature vector of an input test image is compared with each of the feature vectors of database images using weighted Euclidean distance. The gesture is correctly recognized if the distance is the least. This system is tested using a dataset of twenty-four ASL alphabets with three different signers. The experimental results show that the proposed system offers the recognition rate of 91.6 %.


Euclidean distance Feature extraction Hand gesture recognition Human–computer interaction Shape features Sign language recognition 


  1. 1.
    M. Panwar, P.S. Mehra, Hand gesture recognition for human computer interaction, in International Conference on Image Information Processing (2011)Google Scholar
  2. 2.
    T. Ahmed, A neural network based real time hand gesture recognition system. Int. J Comput Appl 59(4) (2012)Google Scholar
  3. 3.
    P.S. Rajam, G. Balakrishnan, Recognition of Tamil sign language alphabet using image processing to aid deaf-dumb people. Proceedia Eng. 30, pp. 861–868 (2011). (SciVerse ScienceDirect, Elsevier)CrossRefGoogle Scholar
  4. 4.
    Md. Atiqur Rahman, Ahsan-Ul-Ambia, Md. Aktaruzzaman, Recognition of Static Hand Gestures of Alphabet in ASL. IJCIT 2(1) (2011) Google Scholar
  5. 5.
    C. Yu, X. Wang, H. Huang, J. Shen, K. Wu, Vision based hand gesture recognition using combinational features, in IEEE Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (2010) pp. 543–546Google Scholar
  6. 6.
    I.G. Incertis, J.G.G. Bermejo, E.Z., Casanova: hand gesture recognition for deaf people interfacing, in The 18th International Conference on Pattern Recognition (2006)Google Scholar
  7. 7.
    A. Pradhan, M.K. Ghouse, M. Pradhan, A hand gesture recognition using feature extraction. Int. J. Curr. Eng. Technol. 2(4) (2012)Google Scholar
  8. 8.
    N.H. Dardas, N.D. Georganas, Real time hand gesture detection and recognition using bag-of-features and support vector machine. IEEE Trans. Instrum. Meas. 60(11) (2011)Google Scholar
  9. 9.
  10. 10.
    L. Lamberti, F. Camastra, Real time hand gesture recognition using a color glove, in Springer 16th International Conference on Image Analysis and Processing (2011), pp. 365–373Google Scholar
  11. 11.
    R. Mapari, G. Kharat, Hand gesture recognition using neural network. Int. J. Comput. Sci. Netw. 1(6) (2012)Google Scholar
  12. 12.
    M.M. Hasan, P.K. Mishra, HSV brightness factor matching for gesture recognition system. Int. J. Image Process. 4(5), pp. 456–467 (2011)Google Scholar
  13. 13.
    A. Karami, B. Zanj, A.K. Sarkaleh, Persian sign language (PSL) recognition using wavelet transform and neural networks. Expert Syst. Appl. 38, 2661–2667 (2011)CrossRefGoogle Scholar
  14. 14.
    D.Y. Huang, W.C. Hu, S.H. Chang, Vision based hand gesture recognition using PCA+ Gabor filters and SVM, in IEEE Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (2009), pp. 1–4Google Scholar
  15. 15.
    J. Singha, K. Das, Hand gesture recognition based on Karhunen-Loeve transform, in Mobile and Embedded Technology International Conference (MECON) (2013), pp. 365–371Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.Department of Computer Science and Engineering, Faculty of Engineering and TechnologyAnnamalai UniversityChidambaramIndia

Personalised recommendations