ASL_Detector is a LSTM model that detects American Sign Language (ASL) gestures and translates them into English using Gemini or Nebius API.

bash

py -3.12 -m venv venv312
(
 Or create .venv outside of folder with the help of VSC IDE:
 - Navigate to .ipynb file
 - On the top right corner, there's a kernel box that displays either your current Python version or the text: "Select Kernel"
 - Click on the kernel box, click on "Select Another Kernel"
 - Click on "Python Environments..."
 - Click on "Create Python Environment"
 - Click on Venv (.venv), choose the Python version that's compatible (3.9 - 3.12)
)
.\venv312\Scripts\activate
(or ..\.venv\Scripts\activate)
pip install -r requirements.txt

python3.11 -m venv venv311
source venv311/bin/activate
pip install -r requirements.txt

Delete cv2.CAP_DSHOW to prevent problems

The main notebook in this project is: asl_alphabet_new.ipynb

It contains 4 main sections:

1. Collecting Data – Capture ASL hand signs using your webcam.
2. Create Dataset – Preprocess and structure the collected data.
3. Train Model – Train a Convolutional Neural Network (CNN) using PyTorch.
4. Live Test – Use your webcam to test the trained model in real-time.

If you want to use the translation part, remember to .env file with your API KEY. It should look like this GEMINI_API_KEY=<your_key> Run new_live_test_3D.py to try the translation from ASL to English.

0. Activate virtual environment

1. Recording video with realtime ASL recognition, then annotate (with both raw and result videos saved afterwards):

py .\live_save_test.py --realtime

2. Annotating existing video: python live_save_test.py --input raw_videos/test2.mp4 --output_dir ./annotated_videos

3. Recording video without realtime ASL recognition, then annotate (with both raw and result videos saved afterwards): py .\live_save_test.py --record

Raw / Input videos will / must be stored in raw_videos Result videos will be generated in annotated_videos