📊 Project Overview

This project, "Hand Gesture Recognition System," focuses on developing a real-time gesture-based communication system using MediaPipe, OpenCV, and LSTM Neural Networks. The system aims to enhance human-computer interaction by accurately recognizing hand gestures, particularly for speech-impaired individuals. By leveraging AI-driven methodologies, the project facilitates gesture-to-text translation, improving accessibility and real-time communication.

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👥 Team Members

Soham Vasudeo Om Agrawal Vanshaj Ajmera Shardul Patki Ritika Shetty Course: Machine Learning Project 2022-23 Institution: NMIMS University

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🎯 Objectives

The objective of this project is to create a robust real-time hand gesture recognition system that can accurately interpret predefined gestures and expand to accommodate new gestures for various applications.

Key Goals: ✔ Enable gesture-based communication for speech-impaired individuals. ✔ Implement a real-time detection system with high accuracy and low latency. ✔ Develop a scalable and adaptable model to recognize additional gestures. ✔ Enhance human-computer interaction through gesture-controlled systems.

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🗂 Data Understanding

The project leverages gesture-based image data to train and optimize the model for accurate recognition.

Dataset Components: 📌 Hand Gesture Images: Captured using MediaPipe Hands for training the model. 📌 Keypoints Extraction: 21 hand landmarks recorded for precise motion tracking. 📌 Predefined Gestures: The system recognizes five primary gestures:

Hello 🖐 I Love You 🤟 Thank You ✋ One ☝ Victory ✌

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📌 Data Storage: Organized in labeled directories for training, validation, and testing.

Challenges Addressed: 🚧 Variations in Lighting & Backgrounds – Improved model robustness to different environments. 🚧 Real-Time Processing Constraints – Optimized inference time for faster recognition. 🚧 Multi-Gesture Recognition – Designed the model to learn new gestures dynamically.

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⚙️ Data Preparation

Data Cleaning & Processing: 🔹 Removed noisy and distorted images. 🔹 Standardized gesture images to a consistent resolution. 🔹 Applied image augmentation (rotation, scaling) to improve model generalization.

Feature Engineering: 🔹 Extracted hand keypoints (x, y, z coordinates) from video frames. 🔹 Converted gesture data into structured NumPy arrays for training.

Data Splitting: 📌 Training Set – 70% 📌 Validation Set – 15% 📌 Testing Set – 15%

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🧠 Modeling

The project utilizes deep learning models to classify hand gestures based on extracted keypoints.

1. Hand Tracking & Feature Extraction: ✅ MediaPipe Hands: Detects 21 keypoints for each hand. ✅ OpenCV Preprocessing: Converts images into feature arrays for analysis.

2. Deep Learning Models for Gesture Classification: 🤖 LSTM (Long Short-Term Memory): Captures temporal dependencies in hand movements. 🤖 CNN (Convolutional Neural Networks): Extracts spatial patterns for accurate recognition. 🤖 ANN (Artificial Neural Networks): Classifies gesture patterns with optimized weight distribution.

3. Model Training & Fine-Tuning: ✔ Hyperparameter Optimization: Adjusted learning rate, batch size, and epochs for best results. ✔ Loss Function & Optimization: Categorical Cross-Entropy with Adam Optimizer.

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📈 Model Evaluation

The performance of the gesture recognition system was evaluated using multiple metrics:

Classification Metrics: 📌 Accuracy: 87.5% on the test dataset. 📌 Precision & Recall: High precision ensures minimal false positives. 📌 F1-Score: Balanced assessment of precision and recall.

Inference Speed: ⏳ Processing Time: ~3.81 ms per frame (real-time execution).

🚀 Deployment Strategy The gesture recognition system is integrated into a real-time application for gesture-to-text translation and smart control systems.

1. Infrastructure Setup: ☁ Compatible with Edge & Cloud Systems – Supports Raspberry Pi, AWS, Google Cloud.

2. Data Processing & Storage: 🔄 Automated Data Collection & Preprocessing for scalability.

3. Model Deployment: ✅ Flask API / FastAPI – Enables real-time communication. ✅ TFLite for Mobile Deployment – Optimized for mobile & embedded devices.

4. Continuous Monitoring & Updates: 📊 Feedback Loop: Allows users to add new gestures dynamically.

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📊 Key Insights and Results

📌 High Recognition Accuracy: 96.4% accuracy on test data. 📌 Fast Processing Speed: Real-time recognition at ~3.81 ms per frame. 📌 User-Friendly Interface: Seamless gesture-to-text translation for assistive communication. 📌 Expandable Gesture Set: New gestures can be added without retraining the entire model.

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🛠 Tools & Technologies Used

Programming & Frameworks: 🖥 Python, NumPy, OpenCV, TensorFlow, Keras

Machine Learning Models: 🧠 LSTM, CNN, ANN

Hand Tracking & Data Processing: 🖐 MediaPipe Hands, OpenCV, NumPy

Visualization & Analytics: 📊 Matplotlib, Seaborn, Power BI

Deployment & Integration: 🌍 Flask, FastAPI, TensorFlow Lite (TFLite)

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📊 Visuals 📸 Dataset Samples: Hand gesture images captured for training.

📊 Model Performance Graphs: Accuracy, loss curves, and real-time testing results.

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🏆 Achievements

🏅 Developed a fully functional real-time gesture recognition system. 🏅 Achieved 96.4% accuracy using deep learning models. 🏅 Successfully integrated MediaPipe for precise hand tracking. 🏅 Enabled real-time gesture classification for assistive communication.