arXiv (All)
open access
Isharah-Selfie: Continuous Sign Language Recognition Dataset for One-handed Signing
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 mobile scenarios. As such, this gap becomes increasingly evident when con
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Traceable Human-to-Humanoid Sign Language Benchmarking#1118066
- SignMatch: Matching Dictionary Signs to Continuous Sign Language Video#822306
- Bridging Communication Gaps: Sign Language Recognition with Deep Learning#105304
- SignBind-LLM: Multi-Stage Modality Fusion for Sign Language Translation#924011
- SignBind-LLM: Multi-Stage Modality Fusion for Sign Language Translation#919782
Record · ID 1455681
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive