Deep Learning-Based Sign Language Recognition from Videos and Cross-Lingual Translation to Indian Vernaculars

Researchers have developed a deep learning pipeline that recognizes sign language gestures from videos and translates them into Indian languages using VideoMAE and Meta's NLLB-200 model. The system achieves 78% validation accuracy on a 13-class dataset and demonstrates practical accessibility applications, though it currently handles isolated words rather than continuous signing.

This research addresses a critical accessibility gap for deaf and hard-of-hearing communities in India by automating sign language recognition and cross-lingual translation. The two-stage architecture leverages recent advances in video transformers and multilingual NLP to bridge communication barriers in low-resource language environments where accessibility tools remain scarce. The VideoMAE fine-tuning approach demonstrates effective transfer learning on limited video data, achieving competitive validation accuracy despite the small dataset size of 197 clips across 13 classes.

The work builds on growing momentum in AI-driven accessibility solutions and represents a broader trend of applying large pretrained models to underserved languages and communities. Sign language recognition has historically struggled due to regional variations, signer-specific styles, and limited annotated datasets—challenges this research acknowledges transparently. The inclusion of Hindi, Telugu, and Bengali reflects India's linguistic diversity and addresses an often-overlooked population in AI development.

For the accessibility and educational technology sectors, this pipeline offers a foundation for building real-world applications that could improve communication access for millions. However, practical deployment faces obstacles: the 78% validation accuracy, confusion between semantically similar signs, and limitation to isolated words rather than continuous discourse. The authors' honest assessment of failure modes and single-signer style sensitivity indicates research maturity over overstated claims.

Future progress depends on scaling to larger, more diverse datasets, expanding vocabulary beyond 13 classes, and handling continuous signing with context awareness. Open-source code release supports community development and iteration. Success here could catalyze broader investment in accessibility AI for other languages and disabilities, establishing sign language recognition as a standard accessibility feature.

• →Deep learning pipeline combining VideoMAE and NLLB-200 achieves 78% validation accuracy on Indian sign language recognition with cross-lingual translation.
• →System handles only isolated 13-word classes from single signers, limiting real-world applicability for continuous communication scenarios.
• →Transparent discussion of failure modes and limitations demonstrates research rigor while highlighting path to production-grade systems.
• →Open-source release enables community contribution to accessibility AI for low-resource Indian languages.
• →Results underscore growing importance of accessibility as an AI application domain but reveal gaps between academic performance and practical deployment.