Sign Language Recognition System using Python | Source Code
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Sign Language Recognition System using Python

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Complete final-year project source code with frontend, backend, database, and setup guide. Instant download after secure payment.

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  • Database script included
  • How-to-run guide
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Project Overview

Description, tech stack, and what is included

Full source Frontend + backend
Database .sql file
Setup guide README included

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

Technical snapshot

Project
Sign Language Recognition System using Python
Stack
PYTHON
Includes
Code, DB, README
License
Academic submission
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Admin Features

Modules and controls available to administrators

  • 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 Features

What end users can do in this application

  • 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

 

Other Features

Additional capabilities included in the project

  • 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

How to Run

Step-by-step setup on your laptop or PC

  • 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.

Login Credentials

Default demo accounts for testing after setup

Admin Login
Username: admin
Password: Admin@123

Demo User Login
Username: alice
Password: User@123

License

Usage terms for academic and personal projects

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Sign Language Recognition System using Python Source Code Final Year PYTHON Project Ready-to-Run Code With Database File Plagiarism-Free Faculty Approved
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