Classification of Static Indian Sign Language Alphabets

  • Abhilash N S, Renson Sebastian, Sreejith Menon M, Dr. Maya L Pai


    We are concentrating more on the use of hand gestures in this paper. In these days, it is hard for a common man to study and remember the hand gestures and to exercise them. But it is the only way for us to communicate with deaf or dump people. In this era, itself the recognition of the hand gestures still persists as a challenge for our modern day machines. The primary issue with the hand gestures recognition is that even the same gestures may differ in posture slightly from one person to another. Also, the amount of lighting and complex backgrounds can also have an impact on the outcome. We collected over 500 images per static alphabet from various sources. The system is developed using ‘Support Vector Machine’ (SVM) for classification along with features extracted using ‘Local Binary Pattern’ (LBP), Local Derivative Pattern’ (LDP) and ‘Histogram of Oriented Gradient’ (HOG) from images and obtained better accuracy (96.66) for the method using HOG. 
How to Cite
Sreejith Menon M, Dr. Maya L Pai, A. N. S. R. S. (2020). Classification of Static Indian Sign Language Alphabets. International Journal of Advanced Science and Technology, 29(3), 5626 - 5632. Retrieved from