Isharah Continuous Sign Language Recognition and Translation Dataset

The first large-scale continuous Saudi Sign Language (SSL) dataset

🧾 About

Isharah is a large-scale dataset for Continuous Saudi Sign Language (SSL) recognition and translation. It features over 30,000 video samples signed by deaf and hearing-impaired individuals using smartphones in varied settings.

The dataset supports both Continuous Sign Language Recognition (CSLR) and Sign Language Translation (SLT), and includes sentence-level gloss annotations and corresponding Arabic translations. Three benchmark subsets are included: Isharah-500, Isharah-1000, and Isharah-2000.

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📜 Usage License

The Isharah Dataset is made available for academic research and educational purposes only. By downloading or using the dataset, you agree to the following terms:

  • ✅ The dataset may be used for non-commercial academic research and educational purposes.
  • ✅ Research papers, reports, theses, and other publications using the dataset must appropriately cite the Isharah dataset paper.
  • ❌ The dataset, or any substantial portion of it, may not be redistributed, republished, sublicensed, or hosted elsewhere without prior written permission from the dataset authors.
  • ❌ The dataset may not be used for commercial purposes, including commercial products, services, or commercial model development, without prior written permission.
  • ❌ Users must not attempt to identify, contact, or otherwise determine the identity of individuals appearing in the dataset.
  • ✅ Models trained using the dataset may be used for academic research and evaluation in accordance with these terms.

For commercial use, redistribution, or other uses not covered by these terms, please contact snalyami@iau.edu.sa

By accessing or using the Isharah Dataset, you acknowledge that you have read and agree to these usage terms.

🏆 Related Challenges

Isharah has been used in the SignEval challenges for benchmarking continuous sign language recognition and related sign language understanding tasks.

SignEval 2026

Multi-Modal Sign Language Recognition and Translation Challenge

SignEval 2025

Multi-Modal Sign Language Recognition Challenge at ICCV 2025

📄 Citation

If you use Isharah in your work, please cite:

@ARTICLE{11397217,
  author={Alyami, Sarah and Luqman, Hamzah and Al-Azani, Sadam and Alowaifeer, Maad and Alharbi, Yazeed and Alonaizan, Yaser},
  journal={IEEE Transactions on Multimedia},
  title={Isharah: A Large-Scale Multi-Scene Dataset for Continuous Sign Language Recognition},
  year={2026},
  volume={},
  number={},
  pages={1-9},
  keywords={Sign Language Recognition;Continuous Sign Language Recognition;Sign Language Translation;Arabic Sign Language;Sign Language Dataset},
  doi={10.1109/TMM.2026.3664959}}