Recognition and classification of deaf signs using neural networks

One approach for deaf signs recognition and classification is presented in the paper. It is assumed that the signs are presented in digital images. Recognition algorithm is consisted of several stages. At the beginning it is necessary to perform appropriate image processing in sense of segmentation and filtration of the input images. Aim is to detect arm position, i.e. sign of interest. For this purpose classifier for skin detection is used. Next stage has to generate feature vectors, which are used as inputs in neural network. Supervised training of neural network is performed. Reduction algorithm was used for purpose of dimension reduction of feature vectors, so the classification results can be displayed graphically.