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Content Provider | IEEE Xplore Digital Library |
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Author | Hemayed, E.E. Hassanien, A.S. |
Copyright Year | 2010 |
Description | Author affiliation: Computer Engineering Department, Faculty of Engineering, Cairo University, Egypt (Hemayed, E.E.; Hassanien, A.S.) |
Abstract | This paper introduces a new hand gesture recognition technique to recognize Arabic sign language alphabet and converts it into voice correspondences to enable Arabian deaf people to interact with normal people. The proposed technique captures a color image for the hand gesture and converts it into YCbCr color space that provides an efficient and accurate way to extract skin regions from colored images under various illumination changes. Prewitt edge detector is used to extract the edges of the segmented hand gesture. Principal Component Analysis algorithm is applied to the extracted edges to form the predefined feature vectors for signs and gestures library. The Euclidean distance is used to measure the similarity between the signs feature vectors. The nearest sign is selected and the corresponding sound clip is played. The proposed technique is used to recognize Arabic sign language alphabets and the most common Arabic gestures. Specifically, we applied the technique to more than 150 signs and gestures with accuracy near to 97% at real time test for three different signers. The detailed of the proposed technique and the experimental results are discussed in this paper. |
Starting Page | 121 |
Ending Page | 127 |
File Size | 1549333 |
Page Count | 7 |
File Format | |
ISBN | 9781612841847 |
e-ISBN | 9781612841854 |
DOI | 10.1109/ICENCO.2010.5720438 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2010-12-27 |
Publisher Place | Egypt |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Arabic sign language Image resolution Image recognition Image color analysis Gesture recognition Skin color segmentation Sign-to-voice |
Content Type | Text |
Resource Type | Article |
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