Glove-Based Continuous Arabic Sign Language Recognition in User-Dependent Mode
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 dataset, hand movements are captured using two DG5-VHand data gloves. Data lab...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | , |
| Format: | article |
| Published: |
2015
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/11073/8820 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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 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 dependency 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 whilst eliminating restrictions of vision-based systems. |
|---|