01 Project Synopsis
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Project Report
Sign Language Recognition System using Python Report
Get structured Sign Language Recognition System using Python Report Final Year Project Report and Documentation with project objectives, methodology, system design, diagrams, implementation details, testing and complete project explanations. Suitable as a learning, documentation and project-presentation resource for students working on related final year projects.
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02 Detailed Project Report
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03 Customized 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
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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