Over past decades, Indian Sign Language plays an important role for speech and hearing impaired community. This paper focus on novel classification for the detection of sign language efficiently with the use of multi features. The purpose of this paper is to study the existing classification and recognition techniques. And to propose the methodology for better results. From the set of images, features such as edge, texture, histogram and corner features are extracted efficiently using Canny edge detection, Gabor filter, and Harris corner detection. These features are categorized by the hybrid techniques of classification by the contribution of LS-SVM with Naïve Bayes classifier. Initially median filter is utilized for the elimination of noise. The segmentation of image is accomplished by utilizing wavelet transform. Then the recognized sentence will be displayed as a text format in the final outcome. The proposed technique implemented and the practical outcome shows high recognition rate and achieve high accuracy of detection.

Canny Edge Detection, Gabor Filter, Harris Corner Detection, LS-SVM, Median Filter, Naïve Bayes, Wavelet Transform

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