Abstract: Highlights•Comprehensive survey of deep learning (DL) in automatic sign language processing (SLP).•Systematic analysis of DL models for SLP tasks: recognition, translation, and production.•In-depth evaluation of model features, performance, strengths, and limitations.•Identified performance gaps between SLP and spoken language processing.•Recommendations for future research to address current SLP limitations.
External IDs:doi:10.1016/j.patcog.2025.111475
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