In this innovative project, I designed and developed a comprehensive real-time hand gesture recognition system that utilizes advanced computer vision and machine learning techniques to recognize hand gestures in real time for various applications.

• PlatformWeb, Real-time Camera Feed
• StackPython, OpenCV, Mediapipe, Scikit-learn
• ModelRandom Forest Classifier

The system is built on a four-layered architecture:

1. Data Collection & Recognition Layer

2. Data Processing Layer

3. Model Training Layer

4. Real-Time Testing Layer

In the Data Collection & Recognition Layer, I integrated **Mediapipe** with a webcam to capture real-time images of hand gestures. The system uses:

• OpenCV (cv2):

  For real-time video capture and image processing, enabling frame analysis for gesture recognition.

• Mediapipe:

  For extracting hand landmarks from images, which are used to classify gestures.

• NumPy:

  Used for handling arrays and numerical operations on feature vectors.

• Pickle:

  For storing preprocessed data and trained models, ensuring efficient reuse.

The Data Processing Layer focuses on transforming captured images into usable data. This involves:

• Mediapipe Hand Landmark Extraction:

  Extracts the 3D landmarks of the hand from the webcam feed, representing key points such as fingertips, palms, and knuckles.

• Data Preprocessing:

  The landmarks are then converted into feature vectors using **NumPy** and saved in **Pickle** files for efficient model training.

The Model Training Layer is responsible for training the systems machine learning model using the preprocessed data. This layer includes:

• Random Forest Classifier:

  The model is trained using a **Random Forest Classifier**, which classifies hand gestures based on the extracted features from the data.

• Scikit-learn:

  This library is used for training and evaluating the model, ensuring optimal performance.

• Pickle:

  The trained model is stored in a **Pickle** file for future use in real-time testing.

In the Real-Time Testing Layer, the system classifies hand gestures in real-time using the webcam feed. The testing layer involves:

• Webcam Feed:

  Captures live video from the webcam, continuously processing each frame to detect and classify gestures.

• Real-Time Prediction:

  The trained model classifies hand gestures on the fly, providing real-time feedback to users.