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Unsupervised Optical Mark Recognition on Answer Sheets for Massive Printed Multiple-Choice Tests.

Yahir Hernández-Mier¹, Marco Aurelio Nuño-Maganda¹, Said Polanco-Martagón¹

¹Intelligent Systems Department, Polytechnic University of Victoria, Ciudad Victoria 87138, Mexico.

Journal of Imaging

|September 26, 2025

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This summary is machine-generated.

This study introduces a desktop application for automated optical mark recognition (OMR) to score multiple-choice tests. The system significantly reduces grading time and improves accuracy compared to manual scoring.

Area of Science:

Computer Science
Image Processing
Educational Technology

Background:

Manual scoring of multiple-choice tests is time-consuming and prone to errors.
Existing optical mark recognition (OMR) algorithms struggle with real-world variations in scanned answer sheets.
Automating the scoring process for large-scale assessments is crucial for efficiency.

Purpose of the Study:

To develop and evaluate a desktop application for optical mark recognition (OMR) of multiple-choice question (MCQ) answer sheets.
To improve the speed and accuracy of scoring standardized tests.
To provide a user-friendly interface for operators to manage and analyze exam results.

Main Methods:

Compiled a dataset of 6029 scanned answer sheets (564,040 four-option answers) from exams administered in Tamaulipas, Mexico.

Keywords:

automatic exam-grading system computer vision image processing

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Developed an image-processing module for answer extraction from digitized answer sheets.

Created an operator interface for folder selection and results tabulation.

Main Results:

The OMR system achieved 96.15% accuracy in grading entire exams without errors.
The system correctly classified 99.95% of individual four-option answers.
Automated scoring averaged 1.04 seconds per answer sheet, compared to 2 minutes for manual scoring.

Conclusions:

The developed OMR desktop application offers a highly accurate and efficient solution for scoring multiple-choice tests.
The system significantly outperforms manual grading in terms of speed and precision.
This technology has the potential to streamline large-scale educational assessments.