AssessIQ - AI-Powered Student Answer Evaluation System :

Automated evaluation of student answers using Natural Language Processing and Computer Vision

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📚 Table of Contents

• Overview
• Features
• Architecture
• Tech Stack
• Installation
• Usage
• API Documentation
• Project Structure
• Scoring Algorithm
• Screenshots
• Contributors

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🎯 Overview

AssessIQ is an intelligent system designed to automate the evaluation of student answer sheets. It combines:

• OCR Technology for extracting text from handwritten answers
• NLP Processing for text normalization and keyword extraction
• Semantic Analysis using Sentence-BERT for meaning-based comparison
• Diagram Evaluation using structural similarity metrics
• Hybrid Scoring algorithm for accurate grading

This project is developed as part of the 6th Semester B.Tech Computer Science Engineering curriculum.

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✨ Features

🔤 Text Extraction (OCR)

• Multi-Engine Support:
  • ✅ EasyOCR (balanced, ~5s)
  • ✅ Ensemble (90-95% accuracy, ~12s)
  • ✅ Tesseract (fast, ~3s)
  • ✅ PaddleOCR (layouts, ~8s)
  • ✅ Sarvam AI Cloud API (cloud, 90-95% accuracy, NEW!)
• Image preprocessing: Noise removal, skew correction, thresholding
• PDF and image file support (PNG, JPG, TIFF, BMP)
• User-selectable OCR engine via API

📝 NLP Processing

• Text cleaning and normalization
• Tokenization and lemmatization
• Stopword removal
• Keyword extraction

🧠 Semantic Analysis

• Sentence-BERT embeddings (all-MiniLM-L6-v2)
• Cosine similarity calculation
• TF-IDF vectorization
• Jaccard similarity

📊 Diagram Evaluation

• SSIM (Structural Similarity Index)
• ORB feature matching
• Contour analysis
• Shape comparison

📈 Scoring System

• Hybrid scoring with dynamic weights
• Keyword coverage analysis
• Length penalty for incomplete answers
• Detailed feedback generation

🎨 Professional UI

• Modern React dashboard
• Material-UI components
• Responsive design
• Animated visualizations

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🏗️ Architecture

┌─────────────────────────────────────────────────────────────────┐
│                        ASSESSIQ SYSTEM                         │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  ┌──────────────┐    ┌──────────────┐    ┌──────────────┐      │
│  │   Frontend   │───▶│   Backend    │───▶│   Database   │      │
│  │   (React)    │◀───│   (FastAPI)  │◀───│   (SQLite)   │      │
│  └──────────────┘    └──────────────┘    └──────────────┘      │
│                              │                                  │
│         ┌────────────────────┴───────────────────┐             │
│         ▼                    ▼                   ▼             │
│  ┌─────────────┐    ┌─────────────────┐   ┌──────────────┐     │
│  │ OCR Service │    │ Semantic Service │   │Diagram Service│   │
│  │  (EasyOCR)  │    │(Sentence-BERT)  │   │   (OpenCV)   │    │
│  └─────────────┘    └─────────────────┘   └──────────────┘     │
│         │                    │                   │              │
│         └────────────────────┴───────────────────┘             │
│                              │                                  │
│                              ▼                                  │
│                    ┌─────────────────┐                         │
│                    │ Scoring Service │                         │
│                    │(Hybrid Algorithm)│                        │
│                    └─────────────────┘                         │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘

Data Flow

Input (Image/PDF/Text)
        │
        ▼
┌───────────────┐
│ OCR Extraction│ ──▶ Extract text from images
└───────────────┘
        │
        ▼
┌───────────────┐
│ NLP Processing│ ──▶ Clean, tokenize, normalize text
└───────────────┘
        │
        ▼
┌───────────────┐
│Semantic Analysis│ ──▶ Generate embeddings, calculate similarity
└───────────────┘
        │
        ▼
┌───────────────┐
│Diagram Analysis│ ──▶ Compare visual elements (if applicable)
└───────────────┘
        │
        ▼
┌───────────────┐
│Hybrid Scoring │ ──▶ Combine scores, apply weights
└───────────────┘
        │
        ▼
    Final Result

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🛠️ Tech Stack

Backend

Technology	Purpose
Python 3.9+	Core programming language
FastAPI	REST API framework
Pydantic	Data validation
SQLAlchemy	ORM for database

AI/ML

Technology	Purpose
EasyOCR	Optical Character Recognition
spaCy	NLP processing
Sentence-Transformers	Semantic embeddings
OpenCV	Image processing
scikit-image	SSIM calculation

Frontend

Technology	Purpose
React 18	UI framework
Material-UI	Component library
Framer Motion	Animations
Axios	HTTP client
Recharts	Data visualization

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📦 Installation

Prerequisites

• Python 3.9 or higher
• Node.js 16+ and npm
• Git

Step 1: Clone Repository

git clone https://github.com/your-username/answer-evaluation.git
cd Answer_Evaluation

Step 2: Backend Setup

# Create virtual environment
python -m venv venv

# Activate virtual environment
# Windows:
venv\Scripts\activate
# Linux/Mac:
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Download spaCy model
python -m spacy download en_core_web_sm

# Download NLTK data
python -c "import nltk; nltk.download('punkt'); nltk.download('stopwords'); nltk.download('wordnet')"

Step 3: Environment Configuration

# Copy example environment file
copy .env.example .env

# Edit .env with your settings (optional)

Step 4: Frontend Setup

cd frontend

# Install dependencies
npm install

# Return to root
cd ..

Step 5: Initialize Database

python -c "from database.models import init_db; init_db()"

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🚀 Usage

Start Backend Server

# From project root
uvicorn api.main:app --reload --host 0.0.0.0 --port 8000

Backend will be available at: http://localhost:8000

Start Frontend Development Server

cd frontend
npm start

Frontend will be available at: http://localhost:3000

Quick Test

# Test API health
curl http://localhost:8000/

# Test with sample evaluation
curl -X POST http://localhost:8000/api/v1/evaluate/text \
  -H "Content-Type: application/json" \
  -d '{
    "model_answer": "Photosynthesis is the process by which plants convert sunlight into energy.",
    "student_answer": "Plants use photosynthesis to make food using sunlight.",
    "question_type": "descriptive",
    "max_marks": 10
  }'

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📖 API Documentation

Endpoints

Method	Endpoint	Description
GET	/	Health check
POST	/api/v1/upload	Upload files for evaluation
POST	/api/v1/evaluate	Evaluate uploaded files
POST	/api/v1/evaluate/text	Evaluate text directly
GET	/api/v1/results	Get all evaluation results
GET	/api/v1/results/{id}	Get specific result
DELETE	/api/v1/results/{id}	Delete result

Interactive Docs

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc

Example: Text Evaluation

Request:

POST /api/v1/evaluate/text
{
  "model_answer": "DNA is deoxyribonucleic acid, which carries genetic information.",
  "student_answer": "DNA stands for deoxyribonucleic acid and contains genetic info.",
  "question_type": "factual",
  "max_marks": 5
}

Response:

{
  "evaluation_id": "abc123",
  "final_score": 85.5,
  "obtained_marks": 4.3,
  "grade": "excellent",
  "score_breakdown": {
    "semantic_score": 0.89,
    "keyword_score": 0.78,
    "diagram_score": null,
    "length_penalty": 0.02
  },
  "concepts": {
    "matched": ["DNA", "deoxyribonucleic acid", "genetic"],
    "missing": ["information", "carries"],
    "coverage_percentage": 75.0
  },
  "explanation": "Good understanding of DNA basics...",
  "suggestions": ["Include more detail about DNA's function"]
}

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📁 Project Structure

Answer_Evaluation/
├── api/                          # Backend API
│   ├── main.py                   # FastAPI application
│   ├── routes/                   # API endpoints
│   │   ├── upload.py             # File upload handling
│   │   ├── evaluation.py         # Evaluation logic
│   │   └── results.py            # Results retrieval
│   └── services/                 # Core services
│       ├── ocr_service.py        # OCR processing
│       ├── nlp_service.py        # NLP operations
│       ├── semantic_service.py   # Semantic analysis
│       ├── diagram_service.py    # Diagram comparison
│       └── scoring_service.py    # Hybrid scoring
│
├── config/                       # Configuration
│   └── settings.py               # Application settings
│
├── database/                     # Database layer
│   └── models.py                 # SQLAlchemy models
│
├── frontend/                     # React frontend
│   ├── public/                   # Static assets
│   └── src/
│       ├── components/           # Reusable components
│       │   └── Layout.jsx        # Main layout
│       ├── pages/                # Page components
│       │   ├── Dashboard.jsx     # Home dashboard
│       │   ├── Evaluate.jsx      # Evaluation form
│       │   ├── Results.jsx       # Results display
│       │   └── History.jsx       # Past evaluations
│       └── services/
│           └── api.js            # API client
│
├── uploads/                      # Uploaded files (gitignored)
├── results/                      # Evaluation results
├── requirements.txt              # Python dependencies
├── .env.example                  # Environment template
└── README.md                     # This file

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📊 Scoring Algorithm

Formula

Final_Score = (Semantic × W₁) + (Keyword × W₂) + (Diagram × W₃) - Length_Penalty

Dynamic Weights by Question Type

Question Type	Semantic (W₁)	Keyword (W₂)	Diagram (W₃)
Factual	0.50	0.40	0.10
Descriptive	0.60	0.30	0.10
Diagram	0.30	0.20	0.50
Mixed	0.45	0.25	0.30

Score Components

1. Semantic Score (0-1): Cosine similarity of Sentence-BERT embeddings
2. Keyword Score (0-1): Percentage of important keywords matched
3. Diagram Score (0-1): SSIM + ORB feature matching
4. Length Penalty (0-0.1): Deduction for too short/long answers

Grading Scale

Grade	Percentage Range
Excellent	≥ 85%
Good	70% - 84%
Average	50% - 69%
Poor	< 50%

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📸 Screenshots

Dashboard

Modern dashboard with statistics and quick actions

Evaluation

Step-by-step evaluation wizard with file upload

Results

Detailed results with animated score visualization

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👥 Contributors

Name	Role	Contact
[Kunal Ekare]	Developer	[kunalekare02@gmail.com]
[Soumya Dhole]	Full Stack Developer	[dholesm@rknec.edu]

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📄 License

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

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🙏 Acknowledgments

• EasyOCR for OCR capabilities
• Sentence-Transformers for semantic embeddings
• FastAPI for the amazing framework
• Material-UI for beautiful React components

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Made with ❤️ for Academic Excellence

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