Hand Gesture Recognition using Machine Learning and Computer Vision

Developed a machine learning-based hand gesture recognition system for classifying hand gestures from images.

The project includes image preprocessing, feature extraction using HOG, dimensionality reduction with PCA, and classification using multiple machine learning models including SVM, Random Forest, and KNN.

The models were evaluated using test data, with SVM achieving approximately 95% test accuracy. A simple Gradio interface was also created to demonstrate the trained model.

Technologies used:

Python, OpenCV, HOG, PCA, SVM, Random Forest, KNN, Gradio, Scikit-learn.

The project demonstrates an end-to-end workflow from image preprocessing and feature extraction to model training, evaluation, and interactive demonstration.