Sign language recognition using PCA, wavelet and neural network

Deaf people all around the world use sign language to communicate and like oral languages vary from country to another so it is for the sign languages. In this paper, we propose a probabilistic neural network (PNN) for two Sign languages: American Sign Language (ASL) recognition for static signs and Arabic sign Language. The signs in both of them are realized with one naked hand and simple background. DCT, DWT and PCA for spatial reduction method. Although PCA has been used before in sign language as a dimensionality reduction technique, it is used here as a descriptor that represents a global image feature. Finally we combine the features to improve the recognition rate (RR) and an error rate(ER) where DWT combined with the PCA using PNN classifier achieves RR 80.2% and ER 3.90% for Arabic database. The RR is improved to be 94% for American database with an ER 1.2%.