https://github.com/tnahom/exam-sheet-evaluator

This project is a computer vision-based system designed to automate the evaluation of exam sheet papers. By utilizing a camera, the system captures the exam sheets, processes the data, and automatically sends the results to students via email.
https://github.com/tnahom/exam-sheet-evaluator

computer-vision flask opencv postgresql python

Last synced: 3 months ago
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This project is a computer vision-based system designed to automate the evaluation of exam sheet papers. By utilizing a camera, the system captures the exam sheets, processes the data, and automatically sends the results to students via email.

• Host: GitHub
• URL: https://github.com/tnahom/exam-sheet-evaluator
• Owner: TNAHOM
• Created: 2023-01-23T16:44:47.000Z (over 3 years ago)
• Default Branch: master
• Last Pushed: 2024-08-18T18:57:20.000Z (almost 2 years ago)
• Last Synced: 2025-02-10T05:16:46.094Z (over 1 year ago)
• Topics: computer-vision, flask, opencv, postgresql, python
• Language: Python
• Homepage:
• Size: 613 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Computer Vision Exam Evaluation System

  ## Overview

  - This project is a computer vision-based system designed to automate the evaluation of exam sheet papers. By utilizing a camera, the system captures the exam sheets, processes the data, and automatically sends the results to students via email. It supports multiple types of questions, including multiple-choice, matching, true-false, and fill-in-the-blank, making it a versatile tool for educational institutions.

  ## Features

  - Automated Exam Evaluation: The system can accurately assess multiple types of questions, ensuring a fair and consistent grading process.

  - Computer Vision Integration: Uses advanced computer vision techniques to interpret the answers marked on physical exam sheets.

  - Email Notifications: Once the exam is evaluated, the results are automatically sent to the respective students via email.

  - Multiple Question Types: The project supports the evaluation of multiple-choice, matching, true-false, and fill-in-the-blank questions.

  ## Tech Stack

  - Backend: Flask (Python), Azure vision

  - Database: PostgreSQL for storing student data and exam results.

  - Deploment: Railway

  - Frontend: HTML, CSS and JS

  - Computer Vision: OpenCV (Python) for processing images and interpreting exam sheets.