Real-time recognition of sign language gestures and air-writing using leap motion

A sign language is generally composed of three main parts, namely manual signas that are gestures made by hand or fingers movements, non-manual signs such as facial expressions or body postures, and finger-spelling where words are spelt out using gestures by the signers to convey the meaning. In literature, researchers have proposed various Sign Language Recognition (SLR) systems by focusing only one part of the sign language. However, combination of different parts has not been explored much. In this paper, we present a framework to recognize manual signs and finger spellings using Leap motion sensor. In the first phase, Support Vector Machine (SVM) classifier has been used to differentiate between manual and finger spelling gestures. Next, two BLSTM-NN classifiers are used for the recognition of manual signs and finger-spelling gestures using sequence-classification and sequence-transcription based approaches, respectively. A dataset of 2240 sign gestures consisting of 28 isolated manual signs and 28 finger-spelling words, has been recorded involving 10 users. We have obtained an overall accuracy of 63.57% in real-time recognition of sign gestures.