A framework to recognize the sign language system for deaf and dumb using mining techniques

Abstract

One way of communicating with the deaf is to speak sign language. The chief barrier to little Indian sign language (ISL) research was the language diversity and variations in place. It is essential to learn sign language to communicate with them. Most learning takes place in peer groups. There are very few materials available for teaching signs. Thus, signing is very challenging to learn. Fingerspelling is the first step in sign learning and is used whenever there is no appropriate sign or if the signatory is unfamiliar with it. Sign language learning tools currently available use expensive external sensors. Through this project, we will take this field further by collecting a dataset and extracting functionally helpful information used in several supervised learning methods. Our current work presents four validated fold cross results for multiple approaches. The difference from the previous work is that we used different figures for our validation set than the training set in four-fold cross-validations.