Segmentation and classification of hand symbol images using classifiers

Abstract The first form of human communication was reliant on signs, which is predominant in mute people; however, numerous variations of gesture-based communication are accessible. This chapter proposes a method for the classification of hand symbols. First, the quality of an acquired image is improved through preprocessing. Then, a segmentation technique is proposed for extraction of the region of interest and a feature extraction algorithm is applied to generate a feature vector. Finally, classifiers are trained with appropriate feature vectors and tested in terms of accuracy. The results of five classifiers are compared on the basis of image features.