Abstract
Indian sign language is used by deaf and dumb persons for communication. Physical materials are not needed for ISL. During this anticipated paper we tend to propose a unique vision-based (VB) methodology for the Indian language (ISL) recognition of isolated signs. The methodology proposed consists of 3 modules: preprocessing, extraction and classification of features. One signer hand is segmented from sign video in segmentation or preprocessing phase. Feature vector is extracted from feature extraction module and show the manual sign parameters used as input for classification. To decrease the computational complexity of the system, a redundant frame algorithmic rule has been applied. The experiment results demonstrate that proposed system achieves 90.3% recognition accuracy based on a lexicon of twenty-one signs of ISL.
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