Dynamic Hand Gesture Recognition System for Kurdish Sign Language Using Two Lines of Features

Hand gesture recognition forms a great difficulty for computer vision especially in dynamics. Sign language has been significant and an interesting application field of dynamic hand gesture recognition system. The recognition of human hands formed an- extremely complicated mission. The solution for such a difficulty requires a robust hand tracking method which depends on an effective feature and classifier. This paper presents a novel, fast and simple method for dynamic hand gesture recognition based on two lines (hundred) of features extracted from two rows of a Real-Time video. Feature selections have been used for hand shape representation to recognize the dynamic word for Kurdish Sign Language. The features extracted in real time from pre-processed hand object were represented through the optimization values of binary captured frame. Finally, an Artificial Neural Network classifier is used to recognize the performed hand gestures by 80% for training and 20% for testing with success 98%.