Spelled sign word recognition using key frame. Issue 5 (1st May 2015)

• Record Type: Journal Article
• Authors: Rokade, Rajeshree S.; Doye, Dharmpal D.
• Is Part Of: IET image processing. Volume 9:Issue 5(2015)
• Page Start: 381
• Page End: 388
• Publication Date: 2015-05-01
• Subjects: gesture recognition -- natural language processing -- feature extraction; spelled sign word recognition -- sign language hand gesture recognition -- video input -- alphabetic sequence -- American Sign Language -- segmentation algorithm -- key frame detection algorithm -- rotation invariant algorithm -- feature extraction; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2012.0691
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : In this study, the authors present a new system for sign language hand gesture recognition. Using video input, the system can recognise any spelled word or alphabetic sequence signed in American Sign Language. The three main steps in the recognition process include detection of the region of interest (the hands), detection of key frames and recognition of gestures from these key frames. The proposed segmentation algorithm distinguishes regions of interest from both uniform and non‐uniform backgrounds with an efficiency of 95%. The proposed key frame detection algorithm achieves an efficiency of 96.50%. A rotation‐invariant algorithm for feature extraction is additionally proposed, which provides an overall gesture recognition efficiency of 84.2%.