Sign Language to Text by Feed Forward Back Propagation Neural Network
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Abstract
In this paper is presented an automatic deaf language to text translation and recognition system. The scheme is based on neural network (NN) classifier using a back propagation. The input parameter vector to neural network is the Fisher score, which represents the derivate of the matrix of symbol probability in hidden Markov model (HMM). The HMM, which needs a sequence to be trained and used, is fed by the hand contour chain code. Besides, an improvement on the calculation of Fisher score is introduced by means of reducing the kernel scores variance. The error ratio classifying hand text of the proposed system is less than 0.8% with our database. The objective of this effort was to explore the utility of a neural network based approach to recognition of the sign language.
Keywords: Computer vision, Human computer interaction, American Sign Language, Image processing, Chain Code, HMM, Fisher score, Neural network, Sign language recognition.
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