Liu, “Hand Gesture Detection and Segmentation Based on Difference Background Image with Complex Background,” Proceedings of the 2008 International Conference on Embedded Software and Systems, Sichuan, 29-31 July 2008, pp. This gesture recognition system can reliably recognize single-hand gestures in real time and can achieve a 90.19% recognition rate in complex background with a “minimum-possible constraints” approach. Training dataset consists of 100 samples of each ASL symbol in different lightning conditions, different sizes and shapes of hand. This paper includes experiments for 26 static hand gestures related to A-Z alphabets. ![]() Least Euclidian distance gives recognition of perfect matching gesture for display of ASL alphabet, meaningful words using file handling. Third stage produces feature vector as centroid and area of edge, which will be compared with feature vectors of a training dataset of gestures using Euclidian distance in the fourth stage. Second stage extracts hand region using blob and crop is applied for getting region of interest and then “Sobel” edge detection is applied on extracted region. First stage converts captured RGB image into binary image using gray threshold method with noise removed using median filter and Guassian filter, followed by morphological operations. This work is divided into four stages such as image preprocessing, region extraction, feature extraction, feature matching. Experimental setup of the system uses fixed position low-cost web camera with 10 mega pixel resolution mounted on the top of monitor of computer which captures snapshot using Red Green Blue color space from fixed distance. In this work, real-time hand gesture system is proposed. Hand gestures are powerful means of communication among humans and sign language is the most natural and expressive way of communication for dump and deaf people.
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