A Signer Independent Sign Language Recognition with Co-articulation Elimination from Live Videos: An Indian Scenario
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-03-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S131915781831228X |
| _version_ | 1819104220628910080 |
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| author | P.K. Athira C.J. Sruthi A. Lijiya |
| author_facet | P.K. Athira C.J. Sruthi A. Lijiya |
| author_sort | P.K. Athira |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T02:02:54Z |
| format | Article |
| id | doaj.art-39557a657792477393858d3b9d433534 |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-12-22T02:02:54Z |
| publishDate | 2022-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-39557a657792477393858d3b9d4335342022-12-21T18:42:36ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782022-03-01343771781A Signer Independent Sign Language Recognition with Co-articulation Elimination from Live Videos: An Indian ScenarioP.K. Athira0C.J. Sruthi1A. Lijiya2Department of Computer Science and Engineering, National Institute of Technology Calicut, Kozhikode, IndiaCorresponding author.; Department of Computer Science and Engineering, National Institute of Technology Calicut, Kozhikode, IndiaDepartment of Computer Science and Engineering, National Institute of Technology Calicut, Kozhikode, IndiaDue 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.http://www.sciencedirect.com/science/article/pii/S131915781831228XSupport vector machinesZernike momentsComputer visionGesture recognitionFeature extractionCo-articulation elimination |
| spellingShingle | P.K. Athira C.J. Sruthi A. Lijiya A Signer Independent Sign Language Recognition with Co-articulation Elimination from Live Videos: An Indian Scenario Journal of King Saud University: Computer and Information Sciences Support vector machines Zernike moments Computer vision Gesture recognition Feature extraction Co-articulation elimination |
| title | A Signer Independent Sign Language Recognition with Co-articulation Elimination from Live Videos: An Indian Scenario |
| title_full | A Signer Independent Sign Language Recognition with Co-articulation Elimination from Live Videos: An Indian Scenario |
| title_fullStr | A Signer Independent Sign Language Recognition with Co-articulation Elimination from Live Videos: An Indian Scenario |
| title_full_unstemmed | A Signer Independent Sign Language Recognition with Co-articulation Elimination from Live Videos: An Indian Scenario |
| title_short | A Signer Independent Sign Language Recognition with Co-articulation Elimination from Live Videos: An Indian Scenario |
| title_sort | signer independent sign language recognition with co articulation elimination from live videos an indian scenario |
| topic | Support vector machines Zernike moments Computer vision Gesture recognition Feature extraction Co-articulation elimination |
| url | http://www.sciencedirect.com/science/article/pii/S131915781831228X |
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