Sign Lanaguage Decipherer by Hand Behavior Image Acquistion using Leapmotion Controller

Electronics Engineering

A sign language decipherer which recognize the hand behavior through the leapmotion controller by acquiring the behavior data by using the feature extraction. Proposed system used Artificial Neural Network (ANN) algorithm with Back Propagation (BP) algorithm to construct a classification model which takes feature as input. In this system, the input hand behavior is performed by verbally challenged persons and is then converted into natural audio. The various set of the hand behaviors can be converted into complete sentence and grammatically corrected using NLTK Library of python. In the next step mapping is done with the pre-recorded audio file which is stored in MP3 module and then the audio output which comprised of natural sentence is interpreted through Galileo board and the sequence of sentence is received as audio output.

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