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ArASL: Arabic Alphabets Sign Language Dataset.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Linguistics

Background:

  • Sign language recognition is crucial for assistive technologies.
  • Limited publicly available datasets exist for Arabic Sign Language (ArSL).
  • Developing automated systems requires comprehensive, labeled data.

Purpose of the Study:

  • To introduce a large, fully-labeled dataset for Arabic Sign Language (ArSL) research.
  • To facilitate the development of machine learning and deep learning models for ArSL recognition.
  • To support research aimed at creating automated systems for the deaf and hard of hearing.

Main Methods:

  • Collection of 54,049 images representing 32 ArSL signs and alphabets.
  • Inclusion of data from 40 participants across various age groups.
  • Dataset named ArSL2018, made publicly available for free research use.

Main Results:

  • A substantial, fully-labeled ArSL image dataset (ArSL2018) has been created.
  • The dataset contains diverse image variations suitable for robust model training.
  • Data preprocessing techniques can address variations in image dimensions and quality.

Conclusions:

  • The ArSL2018 dataset is a valuable resource for advancing ArSL recognition research.
  • Availability of this dataset will accelerate the development of AI-powered tools for the deaf and hard of hearing.
  • This contribution promotes wider research and innovation in sign language technology.