Hand Gesture Recognition using Convolution Neural Network built using Tensorflow, OpenCV and python

hand gesture recognition based on CNN using tensorflow

Hand Gesture Recognition and Modification was based on transfer learning Inception v3 model using Keras with Tensorflow backend trained on 4 classes - rock, paper, scissors, and nothing hand signs. The final trained model resulted in an accuracy of 97.05%. The model was deployed using Streamlit on Heroku Paas.

The project achieves recognition of Indian Sign Language using hand gestures from live video capture. Implemented using Python with OpenCV, TensorFlow and Keras.

Real-time ASL interpreter using OpenCV and TensorFlow/Keras for hand gesture recognition. Features custom hand tracking, image preprocessing, and gesture classification to translate American Sign Language into text and speech output. Built with accessibility in mind.

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.

A simple hand gesture recognition algorithm using tensorflow and mediapipe

Interactive User Interface using Hand Position Gesture Recognition based on Tensorflow.js with React.js.

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 contains Python scripts that use the MediaPipe library to perform hand detection and gesture recognition using a pre-trained tensorflow model.

Fine tuning MobileNet for hand gestures recognition using tensorflow.

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

Machine Learning Lab: Gesture Recognition with IMU Data - A hands-on lab for bachelor students to learn practical ML workflows using TensorFlow

A secure and user-friendly authentication system using hand gesture recognition. Built with TensorFlow, OpenCV, Flask API, and a Tkinter GUI, this project enables gesture-based login with personalized user profiles, real-time video processing, and live authentication logs.

A real-time system that detects and classifies hand gestures via webcam. Using TensorFlow, OpenCV, and CVZone, it processes hand images and predicts gestures with confidence.

Using Tensorflow model SSD_MOBILENET_V2_FPNLITE_320X320 I created hand gesture recognition

🤟 AI-powered ASL learning platform with real-time hand gesture recognition using MediaPipe & TensorFlow | SolutionHacks2025

A real-time hand gesture recognition system built using TensorFlow and a web-based interface. The project classifies hand gestures using a CNN model and provides a seamless UI to test gestures live from your webcam.

This project is a gesture recognition application developed using Python, Flask, OpenCV, MediaPipe, and TensorFlow Keras. It allows users to control computer actions using hand gestures captured from a webcam.

This project is a real-time hand gesture recognition system using MediaPipe, OpenCV, and a TensorFlow/Keras deep learning model. It allows users to perform various hand gestures that are detected live via webcam.

A real-time hand tracking and gesture recognition system using MediaPipe, OpenCV, TensorFlow, React, and Flask. This system demonstrates advanced computer vision techniques with practical applications including audio control, gesture recognition, air drawing, and virtual mouse control.

This project implements a hand gesture recognition system using deep learning techniques with TensorFlow and Keras. It involves preprocessing a dataset from Kaggle, building and training a convolutional neural network (CNN) model.

This repository demonstrates training an hand gesture recognition model using sensor data (accelerometer and gyroscope). The data is pre-processed, normalized, and used to train a model with TensorFlow. s then converted to TensorFlow Lite for efficient deployment on embedded devices.

A real-time hand gesture recognition system that uses deep learning to detect American Sign Language (ASL) characters (0-9, A-Z) from webcam input. The system provides both visual predictions and speech feedback using text-to-speech. Built with OpenCV, TensorFlow, and pyttsx3.

Static hand gesture recognition, using NUS-II hand posture dataset (Python, Tensorflow, CNN)

This repository contains a Python script for real-time hand gesture recognition using TensorFlow Object Detection API. The script uses a pre-trained model for object detection to identify and visualize hand gestures in a live video stream.

Implement using Deep learning, CNN algorithm, mobile net dot SSD, google colab used for training model, TensorFlow API, learned about Object detection.

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Hand gesture recognition system using Python, OpenCV and TensorFlow