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- Complete project source code
- Database / data resources
- Dependencies & configuration
- README / setup guide
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PYTHON Project
Sign Language Recognition System using Python Source Code
Complete final-year project source code with frontend, backend, database and setup documentation. Instant download after secure payment.
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02 Code + Setup Support
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- Everything in Source Code plan
- Remote setup assistance
- Database & configuration setup
- Run verification
- Help via WhatsApp / Email
Project's Overview
SignVision AI is a final year major project for sign language recognition developed using Python and Flask. This AI-based web application detects and predicts sign language gestures through a live webcam or uploaded image using a trained CNN model built with Keras and TensorFlow. The system includes secure user authentication, admin panel, recognition history, model training, dataset management, accuracy reports, PDF/CSV export, and local SQLite database integration. It is a complete machine learning and full-stack web development project suitable for final year college students
Login Credentials
Admin Login
Username: admin
Password: Admin@123
Demo User Login
Username: alice
Password: User@123
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- Secure admin login dashboard
- View total users, active users, predictions and daily usage
- Manage users: activate, deactivate and delete users
- Manage dataset classes and image uploads
- Browse and delete dataset images
- Train and retrain CNN model from admin panel
- View all recognition history records
- Export global history in CSV and PDF format
- Monitor prediction logs and system statistics
- View top predicted signs and recent registered users
- User registration with full validation
- Login using username or email
- Forgot password and reset password option
- Secure profile management
- Dashboard with prediction summary and last login
- Real-time sign language recognition using webcam
- Manual image upload for gesture prediction
- Live confidence score and status updates
- Text output with sentence building options
- Save webcam frame as image
- View personal recognition history
- Search recognition history by sign and date
- Delete selected history or clear all records
- Export personal history in CSV and PDF format
- View accuracy report and confusion matrix
- Help page for user guidance
- AI-based gesture recognition using CNN
- MediaPipe hand region detection support
- Local SQLite database integration
- Keras model training on custom dataset folders
- Automatic support for small and large datasets
- Classification report and per-class statistics
- Bootstrap responsive user interface
- Flask-Login based session handling
- Password hashing with Werkzeug
- CSRF protection using Flask-WTF
- No external API required, fully offline processing
- Suitable for academic and educational use
- Download and extract the project source code.
- Open the project folder in terminal or command prompt.
- Install the required packages using:
pip install -r requirements.txt - Optionally seed the database using:
python seed_db.py --force - Run the Flask application using:
python run.py - Open your browser and visit:
http://127.0.0.1:5000 - For model training, run:
python train_model.py - Login as admin to manage dataset, users and model training.
README Included
Setup Support Available
Admin Login
Username: admin
Password: Admin@123
Demo User Login
Username: alice
Password: User@123