Vision Based Sign Language Identification System Using Facet Analysis (eBook)

Faryal Amber (Autor)

eBook Download: PDF

2014 | 1. Auflage
74 Seiten
GRIN Verlag
978-3-656-69754-1 (ISBN)

Infos

Bachelor Thesis from the year 2013 in the subject Computer Science - Software, grade: A+, , language: English, abstract: The communication gap between the deaf and hearing population is clearly noticed. To make possible the communication between the Deaf and the hearing population and to overpass the gap in access to next generation Human Computer Interfaces, automated sign language analysis is highly crucial. Conversely, an enhanced solution is to build up a conversion system that translates a sign language gestures to text or speech. Exploration and experimentation of an efficient methodology based on facet features analysis. For a recognition system that can recognize gestures from video which can be used as a translation, A methodology has been proposed that extracts candidate hand gestures from sequence of video frames and collect hand features. The system has three separate parts namely: Hand Detection, Shape Matching and Hu moments comparison. The Hand Detection section detects hand through skin detection and by finding contours. It also includes processing of video frames. The procedure of shape matching is attained by comparing the histograms. The values of Hu moments of candidate hand region is identified using contour region analysis and compared to run matches and identify the particular sign language alphabet. Experimental analysis supports the efficiency of the proposed methodology on benchmark data.

Produkt-Details

• Erscheint lt. Verlag: 15.7.2014
• Verlagsort: München
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Software Entwicklung
• Schlagworte: contours • gesture recognition • gesture_recognition • hu_moments_comparison • Hu Moments Comparison • shape matching • shape_matching • sign_language_identification_system • Sign Language Identification System • skin_detection • Skin Detection
• ISBN-10: 3-656-69754-X / 365669754X
• ISBN-13: 978-3-656-69754-1 / 9783656697541