📝 Subjective Answer Evaluation System

This web-based application evaluates scanned subjective answer sheets by comparing student responses with model answers using Optical Character Recognition (OCR) and Natural Language Processing (NLP) techniques. It supports PDF file uploads for both student and model answer sheets.

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🚀 Features

• Upload PDF Answer Sheets: Upload scanned PDF files containing student answers.
• OCR for Text Extraction: Extracts text from the images in the PDF using Tesseract OCR.
• Text Comparison: Compares the extracted text with model answers to evaluate accuracy.
• Model Accuracy Score: The application calculates an accuracy score based on the comparison between the student's answer and the model answer.

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🧰 Tech Stack

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🛠️ Getting Started

To run this project locally, follow these instructions:

✅ Prerequisites

• Python 3.x
• Tesseract OCR installed and configured in PATH
• Required Python libraries (listed in requirements.txt)

📦 Installing Dependencies

1. Clone the repository:

   git clone https://github.com/Praveen-koujalagi/Subjective-Answer-Evaluation-System.git
   cd Subjective-Answer-Evaluation-System

2. Create a virtual environment:

   python -m venv env

3. Activate the virtual environment:

   • On Windows:

     .\env\Scripts\activate

   • On macOS/Linux:

     source env/bin/activate

4. Install the dependencies:

   pip install -r requirements.txt

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▶️ Running the Application

To run the app locally:

streamlit run app.py

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👥 Team

• Praveen Koujalagi
• S Sarvesh Balaji
• Sujit G