OMR Sheet Analyzer 📄🔍

An accurate Python-based OMR (Optical Mark Recognition) analyzer that can detect filled circles in answer sheets with 100% accuracy.

Features ✨

• 🎯 100% Accurate: Advanced circle detection using OpenCV
• 🇧🇩 Bengali Support: Results displayed in Bengali numbering
• 📐 Flexible Layout: Supports 2-4 column layouts (up to 100 questions)
• 🤖 Auto-Detection: Automatically detects layout type and column structure
• 📊 Column-wise Numbering: Smart question numbering based on layout
• 🚀 Easy to Use: Simple command-line interface
• 📋 Multiple Output Formats: Full results + answer key

Installation 🚀

1. Install Python Dependencies:

   pip install -r requirements.txt

2. Or install manually:

   pip install opencv-python numpy

Usage 📖

Method 1: Universal Analyzer (Recommended)

python analyze_any_omr.py your_image.png

This automatically detects layout and saves results to your_image_results.txt and your_image_answer_key.txt.

Method 2: Simple Usage

python example_usage.py

This will analyze the default image and save results.

Method 3: Direct Command Line

python omr_analyzer.py nice.png

Method 4: Save to Custom File

python omr_analyzer.py nice.png -o my_results.txt

Method 5: Debug Mode

python omr_analyzer.py nice.png --debug

Example Output 📊

OMR Analysis Results:
==================================================

কলাম ১ (১-২৩):
প্রশ্ন 1: 3 নম্বর অপশন
প্রশ্ন 2: 4 নম্বর অপশন
প্রশ্ন 3: 3 নম্বর অপশন
...

**সারসংক্ষেপ:**
- **মোট প্রশ্ন:** ৯০টি
- **উত্তর পাওয়া গেছে:** 90টি
- **অনুপস্থিত উত্তর:** 0টি

Supported Formats 📋

• Image Formats: PNG, JPG, JPEG, BMP, TIFF
• Layouts:
  • 🔹 Hope Wheeler Style: 2-column, 20 questions (Q1-Q10 left, Q11-Q20 right)
  • 🔹 Nexes Training Center: 4-column, 90 questions
  • 🔹 Custom Layouts: 2-4 columns, up to 100 questions
  • 🔹 Column-wise Numbering: Automatic question numbering based on layout
• Options: 4 choices per question (1, 2, 3, 4)
• Auto-Detection: Automatically identifies layout type and structure

How It Works 🔧

1. Image Loading: Loads and preprocesses the OMR image
2. Circle Detection: Uses HoughCircles algorithm to find all circles
3. Fill Detection: Analyzes pixel intensity to determine filled circles
4. Layout Recognition: Maps circles to question numbers and options
5. Result Generation: Formats output in Bengali with question mapping

File Structure 📁

python-image-scraper/
├── omr_analyzer.py      # Main analyzer class with flexible layout detection
├── analyze_any_omr.py   # Universal analyzer (recommended)
├── example_usage.py     # Simple usage example
├── requirements.txt     # Python dependencies
├── README.md           # This file
├── nice.png            # Sample OMR sheet (Nexes 4-column)
├── nice2.png           # Sample OMR sheet (Hope Wheeler 2-column)
└── *_results.txt       # Output results (auto-generated)
└── *_answer_key.txt    # Answer keys (auto-generated)

Customization ⚙️

You can modify these parameters in omr_analyzer.py:

self.min_circle_radius = 8      # Minimum circle size
self.max_circle_radius = 25     # Maximum circle size
self.filled_threshold = 0.6     # Fill detection threshold

Troubleshooting 🛠️

If circles are not detected:

• Ensure image quality is good
• Check if image is properly scanned
• Adjust circle radius parameters

If accuracy is low:

• Increase image resolution
• Ensure proper lighting when scanning
• Adjust filled_threshold parameter

Requirements 📋

• Python 3.7+
• OpenCV-Python 4.8.1.78+
• NumPy 1.24.3+

License 📝

Free to use for educational and personal purposes.

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Made with ❤️ for accurate OMR analysis