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A Signer Independent Sign Language Recognition with Co-articulation Elimination from Live Videos: An Indian Scenario.
- Source :
- Journal of King Saud University - Computer & Information Sciences; Mar2022, Vol. 34 Issue 3, p771-781, 11p
- Publication Year :
- 2022
-
Abstract
- Due to the high population of hearing impaired and vocal disabled people in India, a sign language interpretation system is becoming highly important for minimizing their isolation in society. This paper proposes a signer independent novel vision-based gesture recognition system which is capable of recognizing single handed static and dynamic gestures, double-handed static gestures and finger spelling words of Indian Sign Language (ISL) from live video. The use of Zernike moments for key frame extraction reduces the computation speed to a large extent. It also proposes an improved method for co-articulation elimination in fingerspelling alphabets. The gesture recognition module comprises mainly three steps – Preprocessing, Feature Extraction, and Classification. In the preprocessing phase, the signs are extracted from a real-time video using skin color segmentation. An appropriate feature vector is extracted from the gesture sequence after co-articulation elimination phase. The obtained features are then used for classification using Support Vector Machine(SVM). The system successfully recognized finger spelling alphabets with 91% accuracy and single-handed dynamic words with 89% accuracy. The experimental results show that the system has a better recognition rate compared to some of the existing methods. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 13191578
- Volume :
- 34
- Issue :
- 3
- Database :
- Supplemental Index
- Journal :
- Journal of King Saud University - Computer & Information Sciences
- Publication Type :
- Academic Journal
- Accession number :
- 155629884
- Full Text :
- https://doi.org/10.1016/j.jksuci.2019.05.002