CASL-W60: A word-level dataset for central African sign language recognitionKaggle
Sign language is a non-verbal discourse system used by people who are hard of hearing. It also carries cultural context and regional constructs, enabling meaningful communication and often preserving unique traditions. In the Central African region, local sign languages have distinct linguistic cons...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340925005177 |
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| author | Mwaka Lucky Njayou Youssouf Hasan Mahmud Md Kamrul Hasan |
| author_facet | Mwaka Lucky Njayou Youssouf Hasan Mahmud Md Kamrul Hasan |
| author_sort | Mwaka Lucky |
| collection | DOAJ |
| description | Sign language is a non-verbal discourse system used by people who are hard of hearing. It also carries cultural context and regional constructs, enabling meaningful communication and often preserving unique traditions. In the Central African region, local sign languages have distinct linguistic constructs but remain underrepresented in the literature, creating a significant gap in regional word-level datasets for machine learning practitioners. In this research, we present a dataset (CASL-W60) comprising 60 word-level Central African sign language (CASL), collected from 19 volunteers. Each word contains 10–12 video samples per signer, captured following standard African sign language video references. The dataset comprises MP4 video files that are systematically organized and made available through an online repository. We demonstrate its applicability through word-level classification of the 60 sign words. This dataset serves as a valuable resource for developing various applications, including sign language translation, sentence recognition or generation from word-level signs, and sign gloss detection. |
| format | Article |
| id | doaj-art-1101a811a36c4ab2a9a28b2e2dcdbfd6 |
| institution | Kabale University |
| issn | 2352-3409 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj-art-1101a811a36c4ab2a9a28b2e2dcdbfd62025-08-20T03:57:36ZengElsevierData in Brief2352-34092025-08-016111179010.1016/j.dib.2025.111790CASL-W60: A word-level dataset for central African sign language recognitionKaggleMwaka Lucky0Njayou Youssouf1Hasan Mahmud2Md Kamrul Hasan3Systems and Software Lab (SSL), Department of Computer Science Engineering (CSE), Islamic University of Technology (IUT), Board Bazar, Gazipur 1704, Dhaka, BangladeshSystems and Software Lab (SSL), Department of Computer Science Engineering (CSE), Islamic University of Technology (IUT), Board Bazar, Gazipur 1704, Dhaka, BangladeshSystems and Software Lab (SSL), Department of Computer Science Engineering (CSE), Islamic University of Technology (IUT), Board Bazar, Gazipur 1704, Dhaka, BangladeshCorresponding author.; Systems and Software Lab (SSL), Department of Computer Science Engineering (CSE), Islamic University of Technology (IUT), Board Bazar, Gazipur 1704, Dhaka, BangladeshSign language is a non-verbal discourse system used by people who are hard of hearing. It also carries cultural context and regional constructs, enabling meaningful communication and often preserving unique traditions. In the Central African region, local sign languages have distinct linguistic constructs but remain underrepresented in the literature, creating a significant gap in regional word-level datasets for machine learning practitioners. In this research, we present a dataset (CASL-W60) comprising 60 word-level Central African sign language (CASL), collected from 19 volunteers. Each word contains 10–12 video samples per signer, captured following standard African sign language video references. The dataset comprises MP4 video files that are systematically organized and made available through an online repository. We demonstrate its applicability through word-level classification of the 60 sign words. This dataset serves as a valuable resource for developing various applications, including sign language translation, sentence recognition or generation from word-level signs, and sign gloss detection.http://www.sciencedirect.com/science/article/pii/S2352340925005177MediaPipe hand landmarksRegional sign language recognitionVideo framesMachine learning |
| spellingShingle | Mwaka Lucky Njayou Youssouf Hasan Mahmud Md Kamrul Hasan CASL-W60: A word-level dataset for central African sign language recognitionKaggle Data in Brief MediaPipe hand landmarks Regional sign language recognition Video frames Machine learning |
| title | CASL-W60: A word-level dataset for central African sign language recognitionKaggle |
| title_full | CASL-W60: A word-level dataset for central African sign language recognitionKaggle |
| title_fullStr | CASL-W60: A word-level dataset for central African sign language recognitionKaggle |
| title_full_unstemmed | CASL-W60: A word-level dataset for central African sign language recognitionKaggle |
| title_short | CASL-W60: A word-level dataset for central African sign language recognitionKaggle |
| title_sort | casl w60 a word level dataset for central african sign language recognitionkaggle |
| topic | MediaPipe hand landmarks Regional sign language recognition Video frames Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2352340925005177 |
| work_keys_str_mv | AT mwakalucky caslw60awordleveldatasetforcentralafricansignlanguagerecognitionkaggle AT njayouyoussouf caslw60awordleveldatasetforcentralafricansignlanguagerecognitionkaggle AT hasanmahmud caslw60awordleveldatasetforcentralafricansignlanguagerecognitionkaggle AT mdkamrulhasan caslw60awordleveldatasetforcentralafricansignlanguagerecognitionkaggle |