Feature fusion based multilingual fingerspelling recognition

In this paper, we present a fingerspelling recognition module that has been designed to function in a smart system that is intended to act as a communication medium between people with hearing and visual disabilities. The method described is a computer vision based, close to real-time, automatic skin color based model hand gesture recognition module. We analyze and compare the recognition performance of appearance based hand descriptors on a self collected dataset. The dataset contains isolated videos of 88 different signs of the Czech, Turkish and Russian Sign Alphabets from 5 different signers with a total training and test length of 4 hours. On our test sets, we have achieved signer dependent and signer independent fingerspelling recognition rates of %82 and %42, respectively.