Hand Gesture Recognition using Neural Networks and Moment - Dahir, Nizar Saadi (Journal of Kufa for Mathematics and Computer, 2010)
Hand gesture recognition is a rich area of research and covers a wide scope of applications from human machine interaction (e.g in games) to Deaf people computer interface and from 3D animation to control of mechanical systems. In this work a new algorithm for hand gesture recognition is proposed and evaluated. This algorithm uses moment invariants for feature extraction and neural networks for classification. To evaluate the algorithm a subset from the ASL (American Sign Language) is used, this subset consists of the 26 American one-handed sign language alphabet as a training set for the system. The system then tested using images that are different from the training set in size, orientation and position and the results for these tests are presented and discussed.