https://github.com/sumanth44a/custom_hand_sign_recognition

Custom Hand Sign Recognition
https://github.com/sumanth44a/custom_hand_sign_recognition

mediapipe opencv python

Last synced: about 2 months ago
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Custom Hand Sign Recognition

• Host: GitHub
• URL: https://github.com/sumanth44a/custom_hand_sign_recognition
• Owner: sumanth44a
• Created: 2024-06-22T11:40:32.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2024-06-22T12:34:20.000Z (about 2 years ago)
• Last Synced: 2025-03-12T15:14:40.457Z (over 1 year ago)
• Topics: mediapipe, opencv, python
• Language: Python
• Homepage:
• Size: 8.79 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Custom Hand Sign Recognition:

  Sign language detection and recognition are pivotal tasks in computer vision and artificial intelligence, aiding communication for the deaf and hard of hearing community.

# Hand Detection:

  Hand detection involves identifying and locating human hands in images or video frames. It encompasses finding the presence and location of hands within an image or a video stream. Various techniques, including MediaPipe, Haar cascades, and Convolutional Neural Networks (CNNs), along with libraries like OpenCV, are commonly used for hand detection.

# Hand Gesture Recognition:

  Once hands are detected, gesture recognition involves identifying specific hand configurations or movements corresponding to sign language gestures. It is a broader task that not only involves identifying the presence of hands but also recognizing the gestures and translating them into meaningful text or speech. Gesture recognition systems typically leverage machine learning models, such as CNNs, for feature extraction and classification.

# Here's how the process generally works:

  # Hand Detection: 

  Initially, the system scans an image or video frame for potential hand regions using techniques like MediaPipe or Haar cascades. Once a hand is detected, its bounding box or region is identified.

  # Feature Extraction:

  ![image](https://github.com/sumanth44a/Custom_Hand_Sign_Recognition/assets/114097800/6b24d4be-1bb6-4a03-a840-f8d24ba263bb)

    

  For sign language recognition, features are extracted from the detected hand region. These features might include the positions of key hand landmarks, hand shapes, or motion trajectories

  # Gesture Classification:

  ![image](https://github.com/sumanth44a/-Custom-Hand-Sign-Recognition/assets/114097800/082fc9d0-6dfc-4991-93a0-951961271c62)

  

  Following feature extraction, the next step is to classify the extracted features into different sign language gestures. This critical task of recognizing gestures is fundamental for accurate communication. In our implementation, we employed the Random Forest Classifier, a robust machine learning algorithm known for its versatility and performance in classification tasks. Each recognized gesture corresponds to a specific meaning or phrase in sign language.


  
Sign language detection and recognition systems find wide-ranging applications across various domains, serving as invaluable communication aids, educational resources, and interfaces for human-computer interaction. However, it's imperative to address challenges such as accuracy, variability in hand shapes and movements, and real-time performance to ensure the practical deployment and usability of these systems.