This The lack of a visualized representation for standard Arabic Sign Language (ArSL) makes it difficult to do something as commonplace as looking up an unknown word in a dictionary. The majority of printed dictionaries organize ArSL signs (represented in drawings or pictures) based on their nearest Arabic translation; so unless one already knows the meaning of a sign, dictionary look-up is not a simple proposition. In this paper we introduce the ASL database, a large and expanding public dataset containing video sequences of thousands of distinct ArSL signs. This dataset is being created as part of a project to develop an Arabic sign language translator. At the same time, the dataset can be useful for benchmarking a variety of computer vision and machine learning methods designed for learning and/or indexing a large number of visual classes especially approaches for analyzing gestures and human communication.
Samir, A., & Tolba, M. (2015). A Proposed Standardization for Arabic Sign Language Benchmark Database. The Egyptian Journal of Language Engineering, 2(1), 1-9. doi: 10.21608/ejle.2015.60253
MLA
Ahmed Samir; Mohamed Fahmi Tolba. "A Proposed Standardization for Arabic Sign Language Benchmark Database", The Egyptian Journal of Language Engineering, 2, 1, 2015, 1-9. doi: 10.21608/ejle.2015.60253
HARVARD
Samir, A., Tolba, M. (2015). 'A Proposed Standardization for Arabic Sign Language Benchmark Database', The Egyptian Journal of Language Engineering, 2(1), pp. 1-9. doi: 10.21608/ejle.2015.60253
VANCOUVER
Samir, A., Tolba, M. A Proposed Standardization for Arabic Sign Language Benchmark Database. The Egyptian Journal of Language Engineering, 2015; 2(1): 1-9. doi: 10.21608/ejle.2015.60253
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