01 — Pipeline
Holistic landmarks
21 hand × 2, 33 body, and 468 face points per frame from MediaPipe Holistic, fused into a single feature record.
Sign language, made shareable
A bridge between signed and spoken.
BridgeTalk fuses hand tracking, body pose, and facial landmarks to recognize location-anchored signs — touching the chin for thank you, the chest for me, the ear for hear. Real signing is more than gestures in the air; we read where the hand meets the body.
~90
Curated signs
543
Landmarks / frame
26
Alphabet letters (ML)
0
Servers · uploads · tracking
02 — Anchors
14 body regions
Chin, forehead, mouth, nose, ears, cheeks, temples, chest, shoulders, neutral space, lap. Sized to each person's face width.
03 — Contact
Fingertip ↔ region
Proximity with dwell tracking. Distinguishes a sustained touch from an accidental pass-through.
04 — Vocabulary
~90 signs
Location, motion, and shape signs combined: thank you, eat, hear, think, sorry, me, you, plus numbers and fingerspelling fallback.
05 — Sentence
Real-time text
Auto-spaced output with capitalization, punctuation, undo and clear. Read aloud with the device's best voice. History saved locally.
06 — Privacy
Stays in your browser
No server uploads, no tracking. An optional account just stores your name locally. Everything runs in your browser — hand tracking via MediaPipe and the alphabet model via ONNX runtime, with no backend.
.07 — Tunable
Tweakable in-app
Settings panel lets you adjust confidence, hold-time, cooldown, overlay layers, TTS speed, and audio feedback live.
08 — Open
Retrainable model
The alphabet model is a small RandomForest. Train it on real keypoint data with one script and drop the
pickle into models/.
Honest scope
This is a real recognition engine, not a research demo of full sign-language translation. There is no pretrained, browser-ready model in 2026 that recognizes general ASL or PSL conversation — the state of the art needs server-side GPUs and still tops out around 60–70% on a few thousand isolated signs.
What you get here: ~90 signs that work reliably because their multimodal signature (handshape × location × motion) is unambiguous. .
Try these first
01
thank you
Open palm, fingertips touch chin, then move outward.
02
me
Index finger pointing at your own chest.
03
hello
Open palm at temple/forehead, small wave outward.
04
yes
Closed fist, knuckles facing camera, nod down.