Sign language : a systematic review on classification and recognition.
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| Title: | Sign language : a systematic review on classification and recognition. |
|---|---|
| Authors: | Renjith, S1 (AUTHOR) srenjith@am.amrita.edu, Manazhy, Rashmi2 (AUTHOR) |
| Source: | Multimedia Tools & Applications. Sep2024, Vol. 83 Issue 31, p77077-77127. 51p. |
| Subjects: | Artificial intelligence, American Sign Language, Convolutional neural networks, Sign language, Machine learning, Deep learning |
| Abstract: | Sign language serves as an alternative communication mode for those with limitations in hearing and speaking abilities. In India, an estimated population of around 2.7 million people have hearing or speech impairments. Among this population, a significant majority of 98% use sign language as their primary mode of communication. Unfortunately, the limited availability of human interpreters is a considerable obstacle in recognizing and identifying diverse sign languages. To tackle this issue, the present study aims to undertake a thorough examination of various computational methodologies used in various geographical areas for the purpose of categorizing and identifying discrete sign languages. Among the pool of 587 research papers deemed appropriate for qualitative assessment, a total of 95 studies were specifically focused on the categorization and identification of sign language using artificial intelligence-based techniques. The study examines prior research involving deep learning and machine learning methods for a systematic literature review of Sign Language Recognition (SLR). The categorization is made based on language, and the study investigates several facets, including sign type, signing modes, processing techniques, classification methodologies, and evaluation measures. The study reveals that extensive studies were carried out in Chinese, Arabic, and American sign languages. The findings of this review show that among the machine learning techniques, Support Vector Machine (SVM) exhibited higher performance measures, while Convolutional Neural Networks (CNN) exhibited higher performance among the deep learning techniques. [ABSTRACT FROM AUTHOR] |
| Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 179414563 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Sign language : a systematic review on classification and recognition. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Renjith%2C+S%22">Renjith, S</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> srenjith@am.amrita.edu</i><br /><searchLink fieldCode="AR" term="%22Manazhy%2C+Rashmi%22">Manazhy, Rashmi</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Multimedia+Tools+%26+Applications%22">Multimedia Tools & Applications</searchLink>. Sep2024, Vol. 83 Issue 31, p77077-77127. 51p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22American+Sign+Language%22">American Sign Language</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Sign+language%22">Sign language</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Sign language serves as an alternative communication mode for those with limitations in hearing and speaking abilities. In India, an estimated population of around 2.7 million people have hearing or speech impairments. Among this population, a significant majority of 98% use sign language as their primary mode of communication. Unfortunately, the limited availability of human interpreters is a considerable obstacle in recognizing and identifying diverse sign languages. To tackle this issue, the present study aims to undertake a thorough examination of various computational methodologies used in various geographical areas for the purpose of categorizing and identifying discrete sign languages. Among the pool of 587 research papers deemed appropriate for qualitative assessment, a total of 95 studies were specifically focused on the categorization and identification of sign language using artificial intelligence-based techniques. The study examines prior research involving deep learning and machine learning methods for a systematic literature review of Sign Language Recognition (SLR). The categorization is made based on language, and the study investigates several facets, including sign type, signing modes, processing techniques, classification methodologies, and evaluation measures. The study reveals that extensive studies were carried out in Chinese, Arabic, and American sign languages. The findings of this review show that among the machine learning techniques, Support Vector Machine (SVM) exhibited higher performance measures, while Convolutional Neural Networks (CNN) exhibited higher performance among the deep learning techniques. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11042-024-18583-4 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 51 StartPage: 77077 Subjects: – SubjectFull: Artificial intelligence Type: general – SubjectFull: American Sign Language Type: general – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Sign language Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Deep learning Type: general Titles: – TitleFull: Sign language : a systematic review on classification and recognition. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Renjith, S – PersonEntity: Name: NameFull: Manazhy, Rashmi IsPartOfRelationships: – BibEntity: Dates: – D: 25 M: 09 Text: Sep2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 13807501 Numbering: – Type: volume Value: 83 – Type: issue Value: 31 Titles: – TitleFull: Multimedia Tools & Applications Type: main |
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