Real-Time Sign Language Recognition using OpenCV and MediaPipe

This project builds a real-time sign-language recognition system in Python that uses MediaPipe's hand-landmark detection to extract 21 keypoints per hand from a webcam feed, then classifies the hand pose into a corresponding letter/word using a trained machine-learning model.

A dataset of hand-landmark coordinates is collected for each sign in the target alphabet, normalized for hand size and rotation, and used to train a lightweight classifier (Random Forest or a small feed-forward neural network) that runs fast enough for real-time inference on a standard webcam.

Recognized letters are displayed on-screen and can be concatenated into words with a simple debounce/hold-to-confirm mechanism, and the project includes an OpenCV-based training-data-collection script so the sign vocabulary can be extended by the user.

Detailed Project Description

Real-Time Sign Language Recognition using OpenCV and MediaPipe applies computer vision to camera frames to detect or recognise what is in view and act on the result automatically.

How It Works
1. The camera captures frames continuously.
2. Frames are pre-processed and fed to the detection/recognition model.
3. Detections are drawn on the output and used to trigger the required action (log, alert, unlock, count).
4. Results and images are stored or streamed to a browser/dashboard.

Technology Roles
- Python
- OpenCV
- MediaPipe
- Scikit-learn
- Computer Vision

Implementation Steps
1. Define requirements and draw the ER / class diagram and screen flow.
2. Set up the environment (Python, OpenCV, MediaPipe) and create the project skeleton.
3. Design the database schema / data model and seed it with sample data.
4. Implement the core modules with validation and role-based access where needed.
5. Build the user interface and connect it to the backend / data layer.
6. Test each module (unit + manual), fix edge cases and secure inputs (SQL injection, XSS, auth).
7. Prepare documentation, screenshots and a demo build.

Key Features
- Real-time detection
- Annotated output
- Event logging
- Adjustable confidence threshold

Expected Output
On completion the student will have a live video/image feed with detected objects highlighted and the corresponding actions logged.

Deliverables
- Complete source code with comments
- Database schema / data model and screen flow
- Project report with screenshots and results
- Demonstration-ready build