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Ridit07 / repository
Real-time hand gesture recognition system for selective mutism, converting signs into text using computer vision and a Random Forest model, enabling hands-free communication.
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Assistive, real-time hand-gesture-to-text system aimed at helping people with selective mutism (and anyone who prefers hands over voice) communicate using a webcam. The app detects a single hand, recognizes the sign, and builds words/sentences on screen.
People who can’t speak in certain social situations often rely on gestures or writing. This tool turns hand signs into text instantly so conversations can flow in classrooms, workplaces, clinics, or day-to-day life.
Model idea: extract 21 landmark points → normalize → build a 42-feature vector (x,y) → predict letter/word.
git clone https://github.com/Ridit07/Selective-Mutism-Hand-Gesture-To-Text.git
cd Selective-Mutism-Hand-Gesture-To-Text
# create & activate a venv (recommended)
python -m venv .venv
# Windows
.venv\Scripts\activate
# macOS/Linux
source .venv/bin/activate
pip install -r requirements.txt
#If you don’t have a requirements.txt, install the essentials:
pip install opencv-python mediapipe scikit-learn numpy pandas
python app.py
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💡 If your entry file is named differently, run it accordingly:
python main.py
from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier(n_estimators=300, max_depth=None, random_state=42)
clf.fit(X_train, y_train)
# Save model
import joblib
joblib.dump(clf, "models/rf_gesture.pkl")
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