Abstract

The primary purpose of this project is to create a real-time system capable of recognizing hand signs using computer vision and machine learning. This system serves as a proof of concept for how hand gestures can be effectively recognized and interpreted by a machine, which can be extended to more complex applications like sign language translation.This project presents a real-time hand sign recognition system utilizing MediaPipe and TensorFlow. By integrating computer vision and machine learning, the system effectively detects hand landmarks and classifies hand signs using a neural network. The project emphasizes the practical application of hand gesture recognition for purposes such as sign language interpretation, human-computer interaction, and gaming. With an accessible and efficient design, it provides a foundation for developing gesture-based applications on standard hardware.

Methodology

1. Data Collection
   Real-time video input is captured using OpenCV.
   MediaPipe's hand solution extracts 3D hand landmarks (x, y, z) from the input.
   Collected landmarks are paired with manually labeled hand signs and stored in a CSV file for training.
2. Model Training
   A sequential neural network is constructed using TensorFlow/Keras with:
   Dense Layers: For feature extraction.

Activation Functions: ReLU for hidden layers and Softmax for multi-class classification in the output layer.
The dataset is split into training and testing subsets, ensuring robust model evaluation.

3. Real-Time Prediction
   The trained model processes live landmark data from MediaPipe.
   Predicted hand signs are displayed on the screen, demonstrating real-time performance.

GitHub Link

Results

This project demonstrates the potential of using MediaPipe in combination with a neural network to recognize hand signs in real-time. While the solution is effective and relatively easy to implement with standard hardware, its performance depends on the quality of the dataset and can be improved through more sophisticated model architectures or additional data augmentation techniques. This approach offers a cost-effective and accessible alternative to more specialized hardware solutions for hand gesture recognition, making it a valuable tool in various applications ranging from sign language interpretation to gesture-based control systems. However, further improvements in dataset diversity, model complexity, and real-time performance are necessary to make it robust enough for widespread use.

#Key Strengths
Real-Time Processing: Achieved through MediaPipe’s efficient hand tracking.
Cost-Effectiveness: Implements gesture recognition without requiring specialized hardware.
Flexibility: Adaptable for integration into applications like Virtual Reality and Human-Computer Interaction systems.