Title: Machine vision algorithm for MCQ automatic grading – MVAAG

Authors: Aaron Rasheed Rababaah

Addresses: College of Engineering and Applied Sciences, American University of Kuwait, Salmiya, Kuwait

Abstract: Multiple-choice questions (MCQ) predated the first digital computer. MCQ was created as a response to demands for objective and standardised tests for large populations of test takers as in national tests in education, military assessments, surveys, etc. There has been an evolution in the used technology to automate MCQ grading including optical mark recognition (OMR), optical character recognition (OCR), digital image processing (DIP), etc. In this article, we propose a robust solution for MCQ automatic grading using image processing techniques – MVAAG. Our approach uses an unexpansive digital camera or a scanner to scan the answer sheets, which are regular A4 papers. The scanned images are then put through a sequence of DIP operations including colour transformation stages, thresholding, morphology, connected components analysis, etc. MVAAG was validated using extensive experimental testing and found to be effective and efficient compared to manual methods as well as current modern technologies.

Keywords: multiple-choice questions; MCQ; automating MCQ grading; image processing; bubble-based answer sheets processing; machine vision; robust MCQ auto-grading.

DOI: 10.1504/IJCVR.2025.144786

International Journal of Computational Vision and Robotics, 2025 Vol.15 No.2, pp.233 - 251

Received: 12 Apr 2023
Accepted: 08 Nov 2023
Published online: 03 Mar 2025 *

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