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New dataset captures Arabic Sign Language in realistic selfie conditions

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced Isharah-Selfie, a new dataset designed to advance continuous sign language recognition and translation, specifically for Arabic Sign Language (ArSL). Unlike previous benchmarks collected in controlled environments, Isharah-Selfie was captured entirely using front-facing smartphone cameras under unconstrained selfie conditions. The dataset includes nearly 9,000 video clips from 16 signers, covering various domains and featuring one-handed signing, close-up views, and environmental variations. AI

IMPACT This dataset aims to improve sign language recognition technologies for realistic mobile communication scenarios.

RANK_REASON The cluster contains a research paper detailing a new dataset for sign language recognition. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New dataset captures Arabic Sign Language in realistic selfie conditions

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The cluster contains a research paper detailing a new dataset for sign language recognition. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Ahmed Abul Hasanaath, Murtadha Aljubran, Sarah Alyami, Muhammad Haris Khan, Hamzah Luqman ·

    Isharah-Selfie: Continuous Sign Language Recognition Dataset for One-handed Signing

    arXiv:2610.33200v1 Announce Type: new Abstract: Current sign language recognition and translation benchmarks are commonly collected in controlled settings using fixed or studio-style cameras, particularly limiting their ability to reflect how signers communicate in everyday mobil…

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