Latasha1_02mp4

Once extracted, these features are usually saved in structured formats such as:

: If you are using raw video instead of just landmarks, extract Optical Flow features to track the motion intensity between frames. 4. Data Format for Training latasha1_02mp4

: Detailed mesh points to capture "non-manual markers" (facial expressions essential for ASL grammar). Once extracted, these features are usually saved in

: Tracking the shoulders, elbows, and wrists to define the "signing space." 2. Temporal Normalization but for custom feature preparation

: For easy loading into Python-based models.

: 21 points per hand to capture finger articulation and "handshape".

The ASL 1000 dataset is pre-annotated with 2D landmarks, but for custom feature preparation, you can use frameworks like MediaPipe or OpenPose to generate: