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M.Tech Project Report
M.Tech OCR Web Application Final Year 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
OCR Web Application is a full-stack MERN-based OCR project designed to capture screenshots of any website, allow users to crop a specific area, and extract text from the selected region using Optical Character Recognition. The project is useful for developers, students, and content teams who need to quickly extract text from webpage screenshots and visual content.
The application includes URL screenshot capture, an interactive crop tool, multi-pass OCR using Tesseract.js, English and Hindi language support, OCR confidence score, copy-to-clipboard option, PDF export, optional MongoDB persistence, workflow progress UI, responsive design, and Firebase Hosting support.
This project is suitable for B.Tech, M.Tech, BCA, MCA, BE, ME, BSc, and MSc students who need a practical final year project, major project, or minor project based on OCR, image processing, text extraction, screenshot capture, Tesseract.js, MERN stack development, MongoDB persistence, PDF export, and web automation utilities. FileMakr can provide this project with source code, project report, documentation, and setup support for academic submission.
Login Credentials
This application does not include a user login or authentication system. There are no username/password credentials for sign-in
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In this Project Only User Features Exists
- Enter website URL in this final year project
- Capture full-page website screenshot
- Use APIFlash screenshot capture
- Open screenshot in crop modal
- Select specific crop region
- Crop image at full native resolution
- Save cropped image as PNG
- Preview cropped image
- Select OCR language
- Extract text in English
- Extract text in Hindi
- Extract text using English + Hindi mode
- Run multi-pass OCR
- View extracted text
- View word count
- View character count
- View OCR confidence score
- Copy extracted text to clipboard
- Download extracted text as PDF
- Clear extracted text
- Delete cropped image
- Reset complete workflow
- View OCR progress ring
- View error messages
- Use responsive UI on desktop and mobile
- Follow 4-step workflow: Enter URL → Crop → Extract → Export
- Store OCR result in MongoDB through backend API where configured
The Result module supports language selection, text extraction, cropped preview, PDF download, copy button, confidence score, word/character count, progress ring, and backend save integration.
- Full-stack MERN architecture in this final year project
- React 17 frontend
- Node.js backend
- Express.js REST API
- MongoDB database
- Mongoose schema modeling
- APIFlash screenshot capture
- Tesseract.js OCR engine
- React Image Crop integration
- jsPDF PDF generation
- Material-UI components
- Snackbar toast notification
- Custom CSS design system
- Responsive layout
- OCR confidence scoring
- Multi-language OCR mode
- High-resolution screenshot cropping
- Multi-pass OCR extraction
- MongoDB result persistence
- Firebase Hosting deployment support
- Copy extracted text
- Export extracted text as PDF
- 4-step workflow progress UI
The technology stack includes React, Material-UI, Tesseract.js, React Image Crop, jsPDF, Node.js, Express.js, MongoDB, Mongoose, CORS, APIFlash, Firebase Hosting, Create React App, and npm.
Technology Used
| Layer | Technology |
|---|---|
| Frontend | React 17 |
| UI Components | Material-UI v4 |
| OCR Engine | Tesseract.js v5 |
| Image Crop | React Image Crop v8 |
| PDF Export | jsPDF |
| Styling | CSS3 Custom Design System |
| Font | Inter Google Font |
| Backend | Node.js, Express.js |
| Database | MongoDB |
| ODM | Mongoose |
| Middleware | CORS |
| Screenshot API | APIFlash |
| Hosting | Firebase Hosting |
| Build Tool | Create React App |
Step 1: Open Folder
cd OCR-Web-App-master
Step 2: Install frontend dependencies
npm install
Step 3: Install backend dependencies
cd backend
npm install
cd ..
Step 4: Start MongoDB
For Windows:
net start MongoDB
For macOS:
brew services start mongodb-community
For Linux:
sudo systemctl start mongod
For MongoDB Atlas, update the MongoDB connection string in backend/server.js.
Step 5: Configure APIFlash API Key
Open:
src/App/App.js
Replace the API key:
const API_KEY = 'YOUR_APIFLASH_ACCESS_KEY';
The APIFlash key is required for website screenshot capture.
Step 6: Run backend server
Open Terminal 1:
cd backend
node server.js
Expected output:
✅ MongoDB Connected
🚀 Server running on http://localhost:5000
Step 7: Run frontend
Open Terminal 2:
npm start
Frontend opens at:
http://localhost:3000
The project requires two terminals: one for the backend and one for the React frontend.
README Included
Setup Support Available
This application does not include a user login or authentication system. There are no username/password credentials for sign-in