Developing Indian Sign Language Recognition System for Recognizing English Alphabets with Hybrid Classification Approach
M. Suresh Anand, N. Mohan Kumar, A. Kumaresan · Indian Journal of Public Health Research & Development · 2018
Generally speaking and hearing are two simple practices of communication. the deaf and dumb people experiences difficult to communicate with normal people. To resolve this difficulty Indian Sign Language (ISL) recognition system is developed. our Indian Sign Language system uses both hand gesture image and NAM (Non-Audible-Murmur) speech to enhance accuracy of recognition system. From input image hand sign feature are taken by DWT (Discrete-Wavelet-Transform) after preprocessing. And from NAM speech features are taken by MFCC (Mel-Frequency-Cepstral-Coefficients). In this work we implemented fusing the image and audio features and as well as fusing the classification techniques for better recognition. We used HMM (Hidden-Markov-Model) and ANN (Artificial-Neural-Network) for classification. The experimental results shows maximum average recognition rate as 70.19% of the ISLR system while fusing sign image and NAM features with ANN classifier, 79.72% with HMM classifier and 88.84% while fusing classifiers.