Smart Answer Sheet Processor

Intelligent PDF Processing & LMS Automation System

Automate answer sheet processing with ML-powered data extraction and seamless LMS integration

Features • Quick Start • Documentation • API Reference • Contributing

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Overview

Smart Answer Sheet Processor is an end-to-end automation system designed for educational institutions. It leverages machine learning and computer vision to extract student information from answer sheet PDFs and automatically uploads them to the Learning Management System (LMS).

┌─────────────┐    ┌──────────────┐    ┌─────────────┐    ┌─────────────┐
│  PDF Upload │ -> │ ML Extraction│ -> │  Validation │ -> │ LMS Upload  │
└─────────────┘    └──────────────┘    └─────────────┘    └─────────────┘

Features

Core Functionality Feature Description Batch Upload Upload multiple PDFs simultaneously ML Extraction Auto-extract register numbers & subject codes Validation Real-time credential & URL verification LMS Automation Direct upload via Selenium

Advanced Features Feature Description Parallel Processing 3-5 concurrent uploads Real-time Status Live progress monitoring Recheck Config Verify setup without re-upload Modern UI Clean, responsive interface

Tech Stack

Python 3.9+

Django 5.2

MongoDB

PyTorch

OpenCV

Selenium

Technology Matrix

Category	Technology	Version	Purpose
Backend	Django	5.2.7	Web framework & REST API
Database	MongoDB	4.0+	NoSQL document storage
ML/DL	PyTorch	2.1.1	Deep learning models
Vision	OpenCV	4.8.1	Image processing
OCR	Tesseract	5.0+	Text recognition
Automation	Selenium	4.15.2	Browser automation
Detection	Ultralytics	8.0+	YOLO object detection

Requirements

System Requirements Component Minimum Recommended OS Windows 10 / Ubuntu 20.04 Windows 11 / Ubuntu 22.04 Python 3.9 3.11+ RAM 4 GB 8 GB+ Storage 2 GB 5 GB+ GPU - CUDA compatible

Dependencies Package Version django Latest torch Latest opencv-python Latest selenium Latest pymongo Latest ultralytics Latest

Quick Start

1. Clone & Setup

# Clone repository
git clone https://github.com/d-kavinraja/Smart-answer-sheet-processor.git
cd Smart-answer-sheet-processor

# Create virtual environment
python -m venv venv

# Activate (Windows)
venv\Scripts\activate

# Activate (Linux/macOS)
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

2. Database Setup

# Ensure MongoDB is running
mongosh

# Run setup script
python setup_mongodb.py

3. Run Application

cd lms_automation_project

# Apply migrations
python manage.py migrate

# Create admin user
python manage.py createsuperuser

# Start server
python manage.py runserver

4. Access Application

Interface	URL
Web App	http://127.0.0.1:8000
Admin Panel	http://127.0.0.1:8000/admin

Documentation

Workflow

graph LR
    A[Upload PDF] --> B[ML Extraction]
    B --> C{Validate}
    C -->|Pass| D[Upload to LMS]
    C -->|Fail| E[Error Handling]
    D --> F[Complete]

Loading

Status Codes

Status	Description
pending	Awaiting processing
processing	Currently extracting
extracted	Data extracted successfully
uploading	Uploading to LMS
uploaded	Successfully uploaded
failed	Error occurred

API Reference

Endpoints

Method	Endpoint	Description
GET	/	Main dashboard
POST	/api/upload/	Upload PDF files
POST	/api/process/<id>/	Extract data from PDF
POST	/api/recheck/<id>/	Verify configuration
POST	/api/upload-lms/<id>/	Upload to LMS
POST	/api/upload-multiple-lms/	Batch upload
DELETE	/api/delete/<id>/	Delete document
GET	/api/status/<id>/	Get upload status
GET	/api/uploads/	List all uploads

Example Request

# Upload PDF
curl -X POST http://127.0.0.1:8000/api/upload/ \
  -F "pdf_files=@answer_sheet.pdf"

# Check Status
curl http://127.0.0.1:8000/api/status/1/

Example Response

{
  "success": true,
  "data": {
    "id": 1,
    "filename": "answer_sheet.pdf",
    "registerNumber": "212221230038",
    "subjectCode": "19AI505",
    "status": "uploaded",
    "isUploaded": true
  }
}

Project Structure

Smart-answer-sheet-processor/
│
├── lms_automation_project/
│   ├── lms_project/             # Django settings
│   │   ├── settings.py
│   │   ├── urls.py
│   │   └── wsgi.py
│   │
│   ├── pdf_processor/           # Main application
│   │   ├── models.py            # Database models
│   │   ├── views.py             # API endpoints
│   │   ├── urls.py              # URL routing
│   │   └── templates/           # HTML templates
│   │
│   ├── services/                # Core services
│   │   ├── ml_service.py        # ML extraction
│   │   ├── lms_automation.py    # Selenium automation
│   │   └── parallel_lms_uploader.py
│   │
│   ├── models/                  # ML model weights
│   └── media/                   # Uploaded files
│
├── requirements.txt
├── db-setup.md
├── LICENSE
└── README.md

Database Schema

Collections

credentials { "_id": ObjectId, "registerNumber": "212221230038", "username": "22008681", "password": "****", "createdAt": ISODate }

subject_code_urls { "_id": ObjectId, "subject_code": "19AI505", "url": "https://lms...", "createdAt": ISODate }

uploaded_files { "_id": ObjectId, "filename": "file.pdf", "status": "Uploaded", "django_id": 1 }

Troubleshooting

MongoDB Connection Failed

# Check MongoDB status
mongosh

# Windows: Start service
net start MongoDB

# Linux
sudo systemctl start mongod

Credentials Not Found

// Add credentials via mongosh
db.credentials.insertOne({
  registerNumber: "212221230038",
  username: "22008681",
  password: "password123"
})

Port Already in Use

# Use different port
python manage.py runserver 8001

Contributing

We welcome contributions! Please follow these steps:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit changes (git commit -m 'Add AmazingFeature')
4. Push to branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Contributors

License

This project is licensed under the MIT License - see the LICENSE file for details.

Commercial use

Modification

Distribution

Private use

Support

Get Help Check Documentation Report Issues Start Discussion

Resources Django Docs MongoDB Manual PyTorch Tutorials

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Made by Kavinraja D