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Sign language is the most communal language used by the deaf people among themselves for any kind of communication. People who are not familiar with Sign Language face difficulty in interacting with the deaf people.Hence, an effective system should be established to attain and discriminate the sign language. In our proposed system, we developed a framework for extracting and recognizing the sign gesture language and which is classified into four stages like noise removal using median filter, Segmentation using Modified Region Growing Algorithm (MRGA), feature extraction and recognition using Adaptive Genetic Fuzzy Classifier (AGFC). We have used Genetic algorithm with Fuzzy classifier to find out the optimal rules generated by Fuzzy classifier.

Gesture based communication is the fundamental method for correspondence for those with hearing and vocal incapacities. Communication via gestures comprises of making shapes or developments with human hands as for the head or other body parts. In this paper, we propose a new framework for recognizing sign gestures by using Hidden Markov Model (HMM) and Histogram based methods. Initially, the noise of an image will be eliminated by Wiener Filter and the image will be segmented with the help of Histogram oriented methods -Adaptive Histogram technique and then features will be extracted. The extracted features will be given to the HMM for training and recognition of gestures. Our experimental results show a better performance in terms of recognizing gestures from a blurred image compared to the existing segmentation methods.