A framework for sign gesture recognition using improved genetic algorithm and adaptive filter

Gesture based communication is the standard language utilized by the hard of hearing individuals for correspondence purpose. Despite the way that they precisely chat with each other by a method in sign language, they confront obscurity when they attempt to speak with individuals who can see sound, basically with the individuals who can’t understand sign language. Consequently, an effective method to be produced to gain and recognize the sign motion language. In our proposed work we have planned a casing work for examining and distinguishing the sign motion language. The proposed technique is handled through various modules like Noise removal using adaptive filter, segmentation using region growing algorithm and feature extraction by using an improved genetic algorithm. Finally, the proposed technique will be assessed by contrasting with the support vector machine classifier. Subjects: Cognitive Artificial Intelligence; Computational Linguistic & Language Recognition; Human Computer Intelligence; Machine Learning Design