New Methodology for Translation of Static Sign Symbol to Words in Kannada Language

Abstract

is the mean for data transfer over a medium to interact their expressions with each other. The common mode of communication is vocal speech conversation. The main modes of communication are constraint to vocally disabled individuals. For the communication of such individuals various means of communication is suggested, which are called as sign language. Aim: The aim of sign language alphabets recognition is to provide an easy, efficient and accurate mechanism for automatic translation of static sign (determined by a certain configuration of hand) to textual version in kannada language. Problem Statement: The work presented in this paper goal to develop a system for automatic translation of static gestures of alphabets in kannada sign language. It maps letters, words and expression of a certain language to a set of hand gestures enabling an in individual to communicate by using hands gestures rather than by speaking. The system capable of recognizing sign language symbols can be used as a means of communication with hard of hearing people. Sign of the deaf individual can be captured, recognized and translated to words in kannada language for the benefit of blind people. Approach: It has been divided into two phases firstly, feature extraction phase which in turn uses histogram technique, Hough and Segmentation to extract hand from the static sign. Secondly classification phase uses neural network for training samples. Extreme points were extracted from the segmented hand using star skeletonization and recognition was performed by distance signature. Results: The proposed method was tested on the dataset captured in the closed environment with the assumption that the user should be in the field of view. This study was performed for five different datasets in varying lighting conditions. Conclusion: The developed system is focused with objective of reducing the communication gap between normal people and vocally disabled.