A Proposed Standardization for Arabic Sign Language Benchmark Database

Abstract

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.