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Spatio-Temporal Feature-Extraction Techniques for Isolated Gesture Recognition in Arabic Sign Language
(IEEE, 2007)
This paper presents various spatio-temporal feature-extraction techniques with applications to online and offline recognitions of isolated Arabic Sign Language gestures. The temporal features of a video-based gesture are ...
Glove-Based Continuous Arabic Sign Language Recognition in User-Dependent Mode
(IEEE, 2015)
In this paper we propose a glove-based Arabic sign language recognition system using a novel technique for sequential data classification. We compile a sensor-based dataset of 40 sentences using an 80-word lexicon. In the ...
Telescopic Vector Composition and Polar Accumulated Motion Residuals for Feature Extraction in Arabic Sign Language Recognition
(Springer, 2007)
This work introduces two novel approaches for feature extraction applied to video-based Arabic sign language recognition, namely, motion representation through motion estimation and motion representation through motion ...
Novel Feature Extraction and Classification Technique for Sensor-Based Continuous Arabic Sign Language Recognition
(Springer, 2015)
This paper proposes a novel approach to continuous Arabic Sign Language recognition. We use a dataset which contains 40 sentences composed from 80 sign language words. The dataset is collected using sensor-based gloves. ...
User-independent recognition of Arabic sign language for facilitating communication with the deaf community
(Elsevier, 2011)
This paper presents a solution for user-independent recognition of isolated Arabic Sign language gestures. The video based gestures are preprocessed to segment out the hands of the signer based on color segmentation of the ...
Classifying Maqams of Qur'anic Recitations Using Deep Learning
(IEEE Access, 2021)
The Holy Qur’an is among the most recited and memorized books in the world. For beautification of Qur’anic recitation, almost all reciters around the globe perform their recitations using a specific melody, known as maqam ...