models/README.md

Trained model hook (optional)

If you train a sequence classifier and drop it here, the recogniser uses it automatically (in preference to the DTW templates).

Expected files:

File	Purpose
model.h5	Keras model saved with model.save("model.h5")
model.labels.json	JSON list of class names, index-aligned to outputs

Input shape: (batch, 32, 153) — i.e. each sample is a sign segment resampled to TEMPLATE_LEN = 32 frames of the matching vector (pose block + both hands), 9*3 + 21*3*2 = 153 features per frame. This is exactly the array the recogniser passes to model.predict(...) (see recognizer.SignRecognizer._classify_model).

Output: a softmax over the classes listed in model.labels.json. Anything below 0.5 confidence is ignored.

To build a dataset, record signs through the UI (they land in signs/lsf_signs.json as (32, 153) sequences per gloss) and use those as labelled training samples, augmenting with time/scale jitter.