The work presented in this paper goals to develop a system for automatic translation of static gestures of alphabets in American Sign Language. In doing so three feature extraction methods and neural network is used to recognize signs. The system deals with images of bare hands, which allows the user to interact with the system in a natural way. An image is processed and converted to a feature vector that will be compared with the feature vectors of a training set of signs. The system is rotation, scaling of translation variant of the gesture within the image, which makes the system more flexible. The system is implemented and tested using data sets of number of samples of hand images for each signs. Three feature extraction methods are tested and best one is suggested with results obtained from ANN. The system is able to recognize selected ASL signs with the accuracy of 92.33%.
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