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Automatic Multiple Choice Question Evaluation Using Tesseract OCR and YOLOv8

Students & Supervisors

Student Authors
Saikat Mahmud
BSc in Computer Science & Engineering, FST
Kawshik Biswas
BSc in Computer Science & Engineering, FST
Api Alam
BSc in Computer Science & Engineering, FST
Rifat Al Mamun Rudro
Master of Science in Computer Science, FST
Supervisors
Prof. Dr. Kamruddin Nur
Professor, Faculty, FST

Abstract

This paper presents a novel approach for au- tomating the grading of multiple-choice question (MCQ) answer sheets using computer vision and pattern recognition techniques. The system examines student’s marked answer sheet images by comparing with the question sheet image and answer keys. The computer vision and pattern recognition helps extracting pertinent data such as question number detection, MCQ option detection and the answer markings. The proposed approach reliably produces the output report that displays the students’ correct answers with an accuracy of 0.98 F1 score and 0.99 mAP from any form or unstructured question script. This approach can provide a dependable and effective grading system, reducing manual work and offering prompt feedback to students without any constraints on the answer sheets.

Keywords

Optical character recognition (OCR) image processing object detection YOLOv8

Publication Details

  • DOI: 10.1109/CAI59869.2024.00054
  • Type of Publication: Conference
  • Conference Name: 2024 IEEE Conference on Artificial Intelligence (CAI)
  • Date of Conference: 25/06/2024 - 27/06/2024
  • Venue: Marina Bay Sands
  • Organizer: CMA International, National University of Singapore, Nanyang Technological University
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