Open Access

ARTICLE

Fault Tolerant Optical Mark Recognition

by Qamar Hafeez¹, Waqar Aslam¹, M. Ikramullah Lali², Shafiq Ahmad³, Mejdal Alqahtani³, Muhammad Shafiq^4,*

1 Department of Computer Science & IT, The Islamia University of Bahawalpur, 63100, Pakistan
2 Department of Information Sciences, The University of Education, Lahore, 54770, Pakistan
3 Industrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh, 11421, Saudi Arabia
4 Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, Korea

* Corresponding Author: Muhammad Shafiq. Email:

Computers, Materials & Continua 2023, 74(2), 3829-3847. https://doi.org/10.32604/cmc.2023.026422

Received 26 December 2021; Accepted 05 May 2022; Issue published 31 October 2022

Abstract

Optical Mark Recognition (OMR) systems have been studied since 1970. It is widely accepted as a data entry technique. OMR technology is used for surveys and multiple-choice questionnaires. Due to its ease of use, OMR technology has grown in popularity over the past two decades and is widely used in universities and colleges to automatically grade and grade student responses to questionnaires. The accuracy of OMR systems is very important due to the environment in which they are used. The OMR algorithm relies on pixel projection or Hough transform to determine the exact answer in the document. These techniques rely on majority voting to approximate a predetermined shape. The performance of these systems depends on precise input from dedicated hardware. Printing and scanning OMR tables introduces artifacts that make table processing error-prone. This observation is a fundamental limitation of traditional pixel projection and Hough transform techniques. Depending on the type of artifact introduced, accuracy is affected differently. We classified the types of errors and their frequency according to the artifacts in the OMR system. As a major contribution, we propose an improved algorithm that fixes errors due to skewness. Our proposal is based on the Hough transform for improving the accuracy of bias correction mechanisms in OMR documents. As a minor contribution, our proposal also improves the accuracy of detecting markers in OMR documents. The results show an improvement in accuracy over existing algorithms in each of the identified problems. This improvement increases confidence in OMR document processing and increases efficiency when using automated OMR document processing.

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Keywords

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