Hand alphabet recognition using morphological PCA and neural networks

Proposes a method of feature extraction based upon the principal component analysis (PCA) of the pixel positions for the description of the hand postures from colored glove images. We analyze its performance applying it to a neural network based Japanese and American manual alphabet recognition system, while the background remains natural. Average recognition rates of 89.1% for the Japanese and 93.3% for the American fingerspelling has been obtained for a set of 42 Japanese kana and 26 international hand alphabet postures respectively, using a feedforward multilayer perceptron neural neural classifier