01 Synopsis
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MSc Project Report
MSc Sign Language Recognition System using Python Report
Complete MSc final-year project report with documentation, diagrams and viva-ready structure. Instant PDF & Word download — plagiarism-free and faculty-aligned.
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02 Pre Defined Project Report
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Abstract
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Table of Content
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01
Introduction
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02
Problem Statement
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03
Existing System
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04
Proposed System
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05
Objectives
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06
System Architecture
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07
Major Functional Modules
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08
Hardware Requirements
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09
Software Requirements
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10
Future Enhancement
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11
Conclusion
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12
References
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Abstract
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Table of Content
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01
Chapter 1 — Introduction
- 1.1 Background of the Project
- 1.2 Problem Statement
- 1.3 Objectives of the System
- 1.4 Scope of the Project
- 1.5 Existing System Overview
- 1.6 Proposed System Overview
- 1.7 Technologies Used
- 1.8 Overview
- 1.9 Limitations
- 1.10 Advantages
- 1.11 Disadvantages
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02
Chapter 2 — Literature Review / System Study
- 2.1 Introduction
- 2.2 Review of Similar Systems
- 2.3 Comparative Analysis
- 2.4 Software Development Models
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03
Chapter 3 — System Analysis
- 3.1 Introduction
- 3.2 Functional Requirements
- 3.3 Non-Functional Requirements
- 3.4 User Requirements
- 3.5 Feasibility Study
- 3.5.1 Technical Feasibility
- 3.5.2 Economic Feasibility
- 3.5.3 Operational Feasibility
- 3.6 System Architecture
- 3.7 Data Flow Diagram (Level 0)
- 3.8 Data Flow Diagram (Level 1)
- 3.9 Data Flow Diagram (Level 2)
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04
Chapter 4 — System Design
- 4.1 Introduction
- 4.2 Use Case Diagram
- 4.3 Class Diagram
- 4.4 Sequence Diagram
- 4.5 Activity Diagram
- 4.6 ER Diagram
- 4.7 Database Schema
- 4.8 Table Structures
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05
Chapter 5 — System Implementation
- 5.1 Introduction
- 5.2 Development Environment
- 5.3 Tools and Technologies Used
- 5.4 Hardware Requirements
- 5.5 Software Requirements
- 5.6 Module-wise Implementation
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06
Chapter 6 — Testing
- 6.1 Introduction
- 6.2 Testing Strategy
- 6.3 Unit Testing
- 6.4 Integration Testing
- 6.5 System Testing
- 6.6 Test Cases
- 6.7 Bug Reports
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07
Chapter 7 — Results and Discussion
- All screenshots
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08
Chapter 8 — Conclusion and Future Enhancements
- 8.1 Conclusion
- 8.2 Future Enhancements
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09
Chapter 9 — References
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
Related & Popular Reports
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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