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Sign Language to Number by Neural Network
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 40 - Number 10 |
| Year of Publication: 2012 |
| Authors: Shekhar Singh, Pradeep Bharti, Deepak Kumar |
10.5120/5004-7289
|
Shekhar Singh, Pradeep Bharti, Deepak Kumar . Sign Language to Number by Neural Network. International Journal of Computer Applications. 40, 10 ( February 2012), 38-45. DOI=10.5120/5004-7289
Abstract
In this paper is presented an automatic deaf language to number recognition system. Sign language number recognition system lays down foundation for hand shape recognition which addresses real and current problems in signing in the deaf community and leads to practical applications. The scheme is based on neural network (NN) classifier using a back propagation. The input for the sign language number recognition system is 1000 Indian Sign Language number images with 640 x 480 pixels size. 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 number of the proposed system is less than 1.4% with our database. The system learns and recognizes the Indian Sign Language number in training and testing phase using Hidden Markov Model and neural network. The system uses neural network for training and testing phase. The sign language recognizer could recognize Indian sign language number with 98.52% accuracy.
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