Indian sign language recognition using ANN and SVM classifiers
Juhi Ekbote, Mahasweta J. Joshi · 2017
Sign language is an accepted language for communication between deaf and dumb community people. It is the most significant way of communication between normal people and hearing and speech impaired people without the need of an interpreter. Every country has its own developed Sign Language. In India, this dialect is known as Indian Sign Language. This research work aims at developing an automatic recognition system for Indian Sign Language numerals (0-9). The database used for implementation is self-created and consists of 1000 images, 100 images per numeral sign. Shape descriptors, Scale Invariant Feature Transform (SIFT) and Histogram of Oriented Gradients (HOG) techniques are used for extracting desired features. Artificial Neural Networks (ANN) and Support Vector Machine (SVM) classifiers are used to classify the signs. This system achieves accuracy as high as 99%.