A framework for sign gesture recognition using improved genetic algorithm and adaptive filter. Issue 1 (31st December 2016)

• Record Type: Journal Article
• Authors: Kaluri, Rajesh; Pradeep Reddy, Ch.
• Editors: Ai, Qingsong
• Is Part Of: Cogent engineering. Volume 3:Issue 1(2016)
• Publication Date: 2016-12-31
• Subjects: adaptive filter -- feature extraction -- genetic algorithm -- sign language recognition -- speeded-up robust features (SURF); Engineering -- Periodicals; Technology -- Periodicals; Engineering; Technology; Periodicals; 620
• DOI: 10.1080/23311916.2016.1251730
• Languages: English
• ISSNs: 2331-1916

Abstract

Abstract: 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.