Static Hand Gesture Recognition based on DWT Feature Extraction Technique

Every day number of images are generated which implies the necessity to classify, organise and access them using an easy, faster and efficient way. The classification of images into semantic categories is a challenging and important problem now days. Thus hand gesture image detection and recognition is also difficult task. In this paper a novel system which can be used for sign language recognition and interaction with an application or videogame via hand gestures is introduced. This proposed system elucidate framework for vision based recognition of American sign language alphabets from static hand gesture images using Discrete Wavelet Transform based feature extraction technique and Minimum distance vector classifier Daubenchies Wavelet basis function is selected for performing three level two dimensional wavelet decomposition of input static hand gesture image and 7 features are extracted for each image. In the training stage feature vectors are calculated for every training images creating database for classification. In the testing stage input image features are compared with stored trained image features using minimum distance vector classifier.