• Dr ARUL LEENA ROSE

ABSTRACT In this paper a simple and fast algorithm will be developed using orientation histograms to work on a workstation. It will identify static hand signals, namely, American Sign Language (ASL). Previously data gloves or markers have used for input in the system. A pattern recognition system will be using a transform that converts an image into a feature vector, which will then be compared with the feature vectors of a training set of signals. Hand signal recognition procedures with sign language have been used. In sign language, each signal has an assigned meaning. Computer recognition of hand signals may provide a more natural-computer interface, allowing people to point, or rotate a CAD model by rotating their hands. Hand signals can be classified in two categories: static and dynamic. A static signal is a particular hand configuration and poses, represented by a single image. A dynamic signal is a moving signal, represented by a sequence of images. The final system will be implemented with a Perceptron network.

Key words: ASL, Neural Networks, MATLAB, Perceptron

"IDENTIFICATION OF HAND SIGNALS USING ARTIFICIAL NEURAL NETWORKS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 5, page no.492-494, May-2018, Available :http://www.jetir.org/papers/JETIR1805504.pdf

"IDENTIFICATION OF HAND SIGNALS USING ARTIFICIAL NEURAL NETWORKS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 5, page no. pp492-494, May-2018, Available at : http://www.jetir.org/papers/JETIR1805504.pdf