This project implements real-time hand gesture recognition using Python, OpenCV, and MediaPipe. It tracks hand landmarks from a webcam feed and identifies gestures such as Open Palm, Fist, Thumbs Up/Down, Peace Sign, and more. The system supports both left and right hands and displays detected gestures directly on the video stream.

Python uses OpenCV image capture, Mediapipe to realize gesture and human movement detection and recognition; The recognition results are synchronized to Unity in real time to realize the recognition of the moving body structure of hand and character models in Unity

This Python project utilizes the MediaPipe library and OpenCV to perform real-time hand gesture recognition. With this code, you can control your computer's cursor and keyboard using hand gestures.

A powerful real-time AI-driven hand gesture recognition and game control system built using OpenCV, MediaPipe, PyAutoGUI, and custom gesture-classification algorithms. This project enables users to control PC games using hand gestures, removing the need for a keyboard or controller.

A lightweight and efficient Python-based tool for real-time hand gesture recognition using OpenCV and Mediapipe.

Hand Gesture Recognition is a significant area of research in Human-Computer Interaction (HCI) technology. This project demonstrates the development of a real-time Hand Gesture Recognizer using the MediaPipe framework, TensorFlow, and OpenCV in Python.

This repository contains code for a real-time hand gesture recognition system using MediaPipe and OpenCV. The project enables users to control music playback by detecting hand gestures captured through a webcam.

Bit.studio Internship Hand Gesture Recognition using MediaPipe, OpenCV, and Machine Learning

generate training data, train and classify a deep learning model for hand gesture recognition

Hand Gesture Recognition with OpenCV & MediaPipe

Completed by me in April 2024, during my fourth semester in the MS in Data Science program as the Final Research Project. Reviewed 20 papers, tackling hand gesture recognition limitations with MediaPipe's pre-trained models, boosting accuracy and real-time performance over CNN methods. This project received an O grade, scoring 97 out of 100 points.

Musition: Real-time emotion-driven music recommendations system using AI and deep learning. Automated facial and hand gesture data collection and emotion recognition training, reducing manual work by 50%. Tech stack: Streamlit-WebRTC, OpenCV/MediaPipe, TensorFlow/Keras, NumPy, Python, JavaScript.

This repository provides a robust solution for controlling your mouse and audio volume using hand gestures, leveraging the advanced features of Mediapipe and OpenCV. With real-time hand tracking and gesture recognition, users can effortlessly browse through the system and adjust volume levels by simply moving their hand.

The ASL Recognition System is a real‑time American Sign Language (ASL) gesture‑recognition application built with Python, TensorFlow Lite, OpenCV, and MediaPipe. It captures hand landmarks from a webcam, processes them through a lightweight neural network, and instantly translates the gestures into alphabet letters (A‑Z).

A real-time hand raise detection application using MediaPipe, OpenCV, and Streamlit. It detects when a user raises their hand in front of a webcam and provides visual feedback. Ideal for interactive educational sessions, meetings, and gesture recognition experiments.

Real-time gesture recognition system using OpenCV and MediaPipe for detecting hand gestures, facial expressions, and body postures through computer vision.

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PragatiPath AI is an innovative video platform that leverages real-time hand gesture recognition to enhance educational engagement. Using OpenCV and MediaPipe, it interprets hand gestures for interactive content illustration, including drawing, erasing, and changing colors.

🤖 Finger Icon Detection 🤟 A Python project using OpenCV and MediaPipe to track hand gestures in real-time and overlay custom icons above raised fingers (thumb, index, pinky). Perfect for experimenting with hand tracking and gesture recognition.

This is a computer vision project using the OpenCV library integrated with GUI using the PyQt5 library. This project consists of a supermarket analyzer used to analyze supermarket activity and hand gesture recognition using the MediaPipe library. Besides that, this project is also integrated with Firebase as the online database for the user.

Custom Hand Gesture Recognition using OpenCV, mediapipe and sklearn.

Music control with hand gesture recognition using Mediapipe, OpenCV, Tensorflow and PyGame

Real-time hand tracking and gesture recognition using OpenCV and MediaPipe. Includes preprocessing, contour-based hand landmark extraction, and gesture classification for interactive applications like WebAR or robotics.

A touchless air drawing system using computer vision and hand gesture recognition built with Python, OpenCV, and MediaPipe Tasks API.

A real-time Rock-Paper-Scissors game built using Python, OpenCV, and MediaPipe for hand gesture recognition and computer vision interaction.

The *Enhanced Facial Expression and Hand Gesture Recognition System* is a real-time Python application that detects and recognizes facial expressions and hand gestures using a webcam. It leverages powerful libraries like OpenCV, MediaPipe, and Scikit-learn for advanced landmark tracking, gesture classification, and expression recognition.

A real-time Sign Language Detector using computer vision techniques, particularly hand gesture recognition. The system leverages OpenCV for image processing and python-based machine learning frameworks, to recognize hand gestures and translate them into readable text. MediaPipe is used to efficiently track hand movements.

The Gesture-Controlled Car Project allows users to control a car's movement using hand gestures captured by a webcam. By leveraging the power of Python, OpenCV, Mediapipe, and Arduino, this project brings together computer vision, real-time hand gesture recognition, and hardware communication to create an engaging and intuitive user experience.

My latest project: a Hand Gesture Volume Controller using Python, OpenCV, and MediaPipe! 🤚🎶 This innovative project leverages real-time hand tracking and gesture recognition to adjust system volume seamlessly. By detecting the distance between the thumb and index finger, the volume can be controlled intuitively and contactlessly.

This project aims to detect and track hand movements in real-time using a webcam feed. Leveraging Python, OpenCV, and MediaPipe, the system captures hand landmarks and tracks gesture patterns. This can be used for various applications such as gesture-controlled interfaces, sign language recognition, virtual drawing tools, and more.