https://github.com/vignesh010101/hand-gesture-recognition-system

Hand Gesture Recognition model that can accurately identify and classify different hand gestures from image or video data, enabling intuitive human-computer interaction and gesture-based control systems.
https://github.com/vignesh010101/hand-gesture-recognition-system

Last synced: 3 days ago
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Hand Gesture Recognition model that can accurately identify and classify different hand gestures from image or video data, enabling intuitive human-computer interaction and gesture-based control systems.

• Host: GitHub
• URL: https://github.com/vignesh010101/hand-gesture-recognition-system
• Owner: Vignesh010101
• Created: 2024-04-19T05:26:31.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-05-31T06:21:09.000Z (7 months ago)
• Last Synced: 2024-11-06T12:18:17.785Z (about 2 months ago)
• Language: Jupyter Notebook
• Size: 281 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Hand Gesture Recognition System

Hand Gesture Recognition model that can accurately identify and classify different hand gestures from image or video data, enabling intuitive human-computer interaction and gesture-based control systems.

Project description: Developing robust behavior recognition models for better understanding of human-computer interaction and guided control. This work involves the development of machine learning models that can recognize and classify various movements using image or video data.

Project Overview:

-Data Exploration: Analyze and analyze the LeapGestureRecog dataset Kaggle, discover different gestures and their changes.

-Data preprocessing: Perform necessary preprocessing steps such as resizing, normalizing, and augmenting to prepare the dataset for training models.

-Model selection: Choose an appropriate machine learning or deep learning method for gesture recognition. Try different architectures to find the best one.

-Model training: Train the selected model on the proposed data and optimize hyperparameters for better performance.

-Evaluation: evaluate the model's performance, precision, recall, and F1 score using metrics such as precision.

-Visualization: Visualize the model's projection on a sample image or video frame to understand its behavior.

-Consideration of the conversation: If necessary, consider the deployment of the real application to ensure the effectiveness and accuracy of the model in the real situation.

Information:

-Gesture Recognition Technology: Gain expertise in different technologies to recognize and classify gestures.

-Data Preprocessing of Image Data: Get Help - Experience in preprocessing image data, including resizing, normalizing, and enhancing.

-Model selection and hyperparameter transformation: Learn to select and optimize machine learning or deep learning models based on specific action recognition rules.

-Metrics: Understand the importance of various metrics and how they affect your model.

-Visualization Skills: Develop the ability to visualize predictive models and interpret results effectively.

-Applications: Explore real-life applications of gesture recognition, such as human-computer interaction and gesture control.