Gesture recognition using recurrent neural networks
CHI '91 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Recognition of gestures in Arabic sign language using neuro-fuzzy systems
Artificial Intelligence
Color-Based Hands Tracking System for Sign Language Recognition
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Partly-Hidden Markov Model and its Application to Gesture Recognition
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 4 - Volume 4
Skin Colour-Based Face Detection in Colour Images
AVSS '06 Proceedings of the IEEE International Conference on Video and Signal Based Surveillance
An HMM-based call admission control policy for supporting QoS in wireless cellular networks
Computers and Electrical Engineering
Arabic Sign Language Recognition an Image-Based Approach
AINAW '07 Proceedings of the 21st International Conference on Advanced Information Networking and Applications Workshops - Volume 01
Video-based signer-independent Arabic sign language recognition using hidden Markov models
Applied Soft Computing
Hand shape estimation under complex backgrounds for sign language recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Segmentation of the face and hands in sign language video sequences using color and motion cues
IEEE Transactions on Circuits and Systems for Video Technology
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In this paper, we propose an image-based system for Arabic Sign Language (ArSL) recognition. The algorithm starts by detecting the face of the signer using a Gaussian skin color model. The centroid of the detected face is then used as a reference point for tracking the hands' movements. The hands regions are segmented using a region growing algorithm assuming the signer wears a yellow and an orange colored gloves. From the segmented hands regions, an optimal set of features is extracted. To represent the time varying feature patterns, a Hidden Markov Model (HMM) is then used. Before using HMM in testing, the number of states and the number of Gaussian mixtures are optimized. The proposed system was implemented for both signer dependent and signer independent conditions. The experimental results show that an accuracy of more than 95% can be achieved with a large database of 300 signs. The results outperform previous work on ArSL mainly restricted to small vocabulary size.