Upload a BSL video for English text. Speak or type English for BSL signing. Built for Deaf communities, researchers, and accessibility.
Vision, language, speech, and animation unified into a single pipeline. Every output is shown as text—nothing relies on audio.
Upload signing video. Video-SWIN-T recognises signs across 5,203 classes and translates to natural English.
Type or speak English. The system generates BSL glosses and renders animated skeleton-signing video.
Groq gpt-oss-120b converts BSL glosses into fluent, grammatically correct English sentences.
Coqui XTTS v2 synthesises natural speech from translated text using voice cloning with speaker reference.
Trained on BSLDict with retrieval-based recognition achieving perfect Top-1 accuracy on dictionary signs.
Plain language, high contrast, keyboard navigation, text-first output. Designed for BSL users.
Translate between BSL glosses and English instantly. For the full system with video recognition, speech, and signing animation — explore the full demo.
Video recognition, camera input, speech output & signing animation
Benchmark results across recognition models and translation components.
| Model | Language | Top-1 | Top-5 |
|---|---|---|---|
| BSL Dict Retrieval | British | 100% | 100% |
| BSL-100 | British | 72.34% | 95.03% |
| BSL-500 | British | 59.26% | 89.04% |
| Pose Recognition | ASL | 44.44% | 81.62% |
| Multi-Lingual | ASL+LSF | 20.95% | 49.17% |
End-to-end pipeline unifying vision, language, speech, and animation.
| Component | Technology | Details |
|---|---|---|
| Sign Recognition | Video-SWIN-T | Retrieval on 5,203 pre-extracted 768-dim features |
| Speech Recognition | OpenAI Whisper | Base model, 16 kHz mono |
| Text-to-Speech | Coqui XTTS v2 | Voice cloning with speaker reference |
| Language Model | Groq gpt-oss-120b | Gloss to natural English |
| Signing Animation | 2D Pose Animator | Skeleton signing with MP4 export |
| Vocabulary | 11,573+ glosses | BSL-1K + BSLDict datasets |
Cosine similarity on 768-dim SWIN features achieves perfect accuracy across 5,203 BSL dictionary signs with one sample per class.
Pre-computing features for all 5,203 videos takes ~1 hour on RTX 4060 (8 GB). Inference is near-instant after extraction.
Vision, language, speech, and animation integrated in one system with consistent API patterns and shared vocabulary.
Researchers, engineers, BSL interpreters, and deaf community members — your insights help shape the next version.
Open feedback form ↗Real-time BSL signing overlay for YouTube, BBC iPlayer, Netflix and more. Free to download.
Download Beta →Signs what your computer is saying, in any app. Windows and Mac.