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Android: ship pre-trained — bundle templates + lexicon from signs/
Gradle seedAssets copies signs/{lexicon,lsf_signs}.json into assets at build time (single source of truth); MainActivity seeds the on-device store on first launch. APK verified building (67 MB, 143-sample model bundled). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>de48e34 -
Add 2nd Elix video per word (143 samples) + one-command run.sh
- elix_train.py: --skip/--count, re-encode dedupe; 62 words now have 2 samples (143 total across 72 glosses) for better DTW generalisation. - The Python web app + CLI already load signs/lsf_signs.json, so they're pre-trained out of the box. Add run.sh: venv bootstrap + serve in one command. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>3b55737
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Add native Kotlin Android app (on-device, MediaPipe HolisticLandmarker)
Fully on-device LSF interpreter: - CameraX preview + front/back camera selector (selfie/main), front mirrored. - MediaPipe HolisticLandmarker on each frame (fingers/eyes/mouth/chest/arms). - Skeleton overlay (hand/pose lines, face dots). - DTW learn-by-example recognizer + segmentation + persistence, ported from the Python engine (identical thresholds); ~80-word LSF lexicon bundled. - Native dual-pixel/depth detection via Camera2 DEPTH_OUTPUT capability. Not compiler-verified (no Android toolchain here): open in Android Studio, run android/fetch_model.sh for the .task model, then build the APK. Excluded from the Docker/HF build context. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>fe3ddd9