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Hand Gesture Recognition using Deep Learning
This repository contains the code for a real-time hand gesture recognition system developed as a 5th-semester minor project at Mount Carmel College. The system uses computer vision and various machine learning models to identify and classify hand gestures from a live video feed.
Project Objective
The goal of this project is to build and compare different models for hand gesture recognition. We explore both deep learning and traditional machine learning approaches to find an effective solution for classifying static hand gestures captured via a webcam.
Features
- Real-time Detection: Identifies gestures from a live webcam feed.
- Multiple Models: Implements and compares three different classification models:
- Convolutional Neural Network (CNN)
- K-Nearest Neighbors (KNN)
- Support Vector Machine (SVM)
- Jupyter Notebooks: Clear, separated notebooks for each model's training and implementation.
Tech Stack & Dependencies
- Python 3.x
- OpenCV: For video capture and image processing.
- TensorFlow / Keras: For building and training the CNN model.
- Scikit-learn: For implementing the KNN and SVM models.
- NumPy: For numerical operations.
- Jupyter Notebook: For code development and demonstration.
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