Continuous dynamic Indian Sign Language gesture recognition with invariant backgrounds

Hand gestures are a strong medium of communication for hearing impaired society. It is helpful for establishing interaction between human and computer. In this work we proposed a continuous Indian Sign Language (ISL) gesture recognition system where single hand or both the hands have been used for performing gestures. Our proposed method is also invariant against various backgrounds. Tracking useful frames of gestures from continuous frame, frame overlapping method has been applied. Here we extract those frames which contains maximum information. This is helpful for speedup the recognition process. After that discrete wavelet transform (DWT) is applied for extracting features of an image frame and finally hidden markov model (HMM) is used for testing probe gestures. Experiments are performed on our own continuous ISL dataset which is created using canon EOS camera in Robotics and Artificial Intelligence laboratory (IIIT-A). From experimental results we have found that our proposed method works on various backgrounds like colored background, a background containing multiple objects etc. also this framework provides vary less time complexity as well as space complexity.