Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Real-Time American Sign Language Recognition Using Desk and Wearable Computer Based Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition of gestures in Arabic sign language using neuro-fuzzy systems
Artificial Intelligence
HMM-Based Continuous Sign Language Recognition Using Stochastic Grammars
GW '99 Proceedings of the International Gesture Workshop on Gesture-Based Communication in Human-Computer Interaction
A Real-Time Large Vocabulary Recognition System for Chinese Sign Language
GW '01 Revised Papers from the International Gesture Workshop on Gesture and Sign Languages in Human-Computer Interaction
Toward Robust Skin Identification in Video Images
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Locating Facial Region of a Head-and-Shoulders Color Image
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Relevant Features for Video-Based Continuous Sign Language Recognition
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
An Approach Based on Phonemes to Large Vocabulary Chinese Sign Language Recognition
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Automatic Recognition of Colloquial Australian Sign Language
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
A Chinese sign language recognition system based on SOFM/SRN/HMM
Pattern Recognition
Face segmentation using skin-color map in videophone applications
IEEE Transactions on Circuits and Systems for Video Technology
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Most of the existing work on Arabic sign language (ArSL) recognition focuses on static gestures, while there is a growing need for recognition of continuous gestures. In this work, we develop a system that makes automatic translation of dynamic gestures in the Arabic Sign Language (ArSL) using two stages (Hierarchical) scheme. The system is composed of two stages: the first stage recognizes the group of the gesturer and the second stage recognizes the gestures within the groups. Spatial domain analysis is used for features extraction from the hands and face regions, which are classified using Hidden Markov Model (HMM). The extracted features include eccentricity of the hand region, coordinate of the center of the hand region, direction angle of the hand region, and the hand vector that represents the shape of the hand. These features are scale and translation invariant. We have used two types of features: simple and complex. The simple features comprise six features and the complex comprises 17 features. The complex features include 11 hand vectors which are not included in the simple features. The recognition rate for the signer-dependent is 92.5% and for the signer-independent is 70.5%.