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Glove-Based Continuous Arabic Sign Language Recognition in User-Dependent Mode

Glove-Based Continuous Arabic Sign Language Recognition in User-Dependent Mode

Starting at: Rs.4,500.00

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Glove-Based Continuous Arabic Sign Language Recognition in User-Dependent Mode

 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 dataset, hand movements are captured using two DG5-VHand data gloves. Data labeling is performed using a camera to synchronize hand movements with their corresponding sign language words. Low-complexity preprocessing and feature extraction techniques are applied to capture and emphasize the temporal dependence of the data. Subsequently, a Modified k-Nearest Neighbor (MKNN) approach is used for classification. The proposed MKNN makes use of the context of feature vectors for the purpose of accurate classification. The proposed solution achieved a sentence recognition rate of 98.9%. The results are compared against an existing vision-based approach that uses the same set of sentences. The proposed solution is superior in terms of classification rates while eliminating restrictions of vision-based systems.


 


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  • Model: PROJ5238
  • 999 Units in Stock
  • Manufactured by: ClickMyProjects

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This product was added to our catalog on Wednesday 14 September, 2016.

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