Evaluation System for Multiple-Choice Questions Using Optical Mark Recognition: A Survey

Authors

  • Eman R. Ali Department of computer science, Collage of science, Mustansiriyah University, Baghdad, Iraq
  • Narjis M. Shati Department of computer science, Collage of science, Mustansiriyah University, Baghdad, Iraq

DOI:

https://doi.org/10.29304/jqcsm.2025.17.11975

Keywords:

OMR, Optical Mark Recognition, Assessments, MCQ, Answer sheet

Abstract

performing bulk assessment corrections across various domains and applications can be an expensive and time-consuming task. Optical Mark Recognition (OMR) technology can be used to speed up this process. It is an automated data input method that captures the existence or absence of different marks (filled circles, crosses, and ticks) on printed papers, such as multiple-choice exams. OMR was originally introduced as a dedicated hardware solution and has since evolved into software solutions. However, many of these software solutions lack flexibility, particularly for the end users. This work reviews different papers related to the OMR topic and outlines their key features, datasets, processing time and accuracy. The goal of this review is to highlight areas in OMR that still require further research and development. In conclusion, areas where future research efforts should be directed are identified

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References

PaperSurvey.io. "What Is OMR?" PaperSurvey.io Blog. Last modified February 7, 2023. https://www.papersurvey.io/blog/what-is-omr.

Dhawaleswar Rao, C. H., and Sujan Kumar Saha. "Automatic Multiple Choice Question Generation from Text: A Survey." *IEEE Transactions on Learning Technologies* 13, no. 1 (January-March 2020): 10-22. https://doi.org/10.1109/TLT.2020.8585151.

Sanguansat, Parinya. "Robust and Low-Cost Optical Mark Recognition for Automated Data Entry." In *Proceedings of the 2015 IEEE International Conference on Information and Communication Technology for Embedded Systems (IC-ICTES)*, 978-1-4799-7961-5. IEEE, 2015.

Hafeez, Qamar, Waqar Aslam, M. Ikramullah Lali, Shafiq Ahmad, Mejdal Alqahtani, and Muhammad Shafiq. "Fault Tolerant Optical Mark Recognition." *Computers Materials & Continua* 74, no. 2 (October 2022). https://doi.org/10.32604/cmc.2023.026422.

de Elias, Erik Miguel, Paulo Marcelo Tasinafo, and R. Hirata Jr. "Optical Mark Recognition: Advances, Difficulties, and Limitations." *SN Computer Science* 2, no. 367 (2021). https://doi.org/10.1007/s42979-021-00760-z.

Patel, Nirali, and Ghanshyam I. Prajapati. "Various Techniques for Assessment of OMR Sheets through Ordinary 2D Scanner: A Survey." *International Journal of Engineering Research and Technology* 4 (2015): n.p.

Karunanayake, Nalan. "OMR Sheet Evaluation by Web Camera Using Template Matching Approach." *International Journal for Research in Emerging Science and Technology* 2, no. 8 (2015): 40-44.

Abdul Nabi, Abrar H., and Inad A. Aljarrah. "An Automated Multiple Choice Grader for Paper-Based Exams." In *Advances in Machine Learning and Signal Processing*, edited by P.J. Soh et al., 387. *Lecture Notes in Electrical Engineering*. Springer International Publishing, 2016. https://doi.org/10.1007/978-3-319-32213-1_19.

Bayar, Gökhan. "The Use of Hough Transform to Develop an Intelligent Grading System for the Multiple Choice Exam Papers." *Karaelmas Fen ve Mühendislik Dergisi* 6, no. 1 (2016): 100–104.

Catalan, Jose Antonio. "A Framework for Automated Multiple-Choice Exam Scoring with Digital Image and Assorted Processing Using Readily Available Software." Paper presented at the *DLSU Research Congress 2017*, De La Salle University, Manila, Philippines, June 20-22, 2017.

Alomran, M., and D. Chai. "Automated Scoring System for Multiple Choice Test with Quick Feedback." *International Journal of Information and Education Technology* 8, no. 8 (2018): 538. https://doi.org/10.18178/ijiet.2018.8.8.1096.

“An Image Processing Oriented Optical Mark Recognition and Evaluation System.” *International Journal of Applied Methods in Electronics and Computers* 6, no. 4 (2018): 59-64. https://ijamec.org/index.php/ijamec/article/view/261.

Kumar, Amit, Himanshu Singal, and Arnav V. Bhavsar. "Cost Effective Real-Time Image Processing Based Optical Mark Reader." (2018).

Akhter, Jesmin, Fariha Afsana, Manan Binth Taj Noor, and K. M. Akkas Ali. "Cost-Effective Small-Scale Mark Recognition Technique in Evaluation of OMR Sheet." *Jahangirnagar University Journal of Science* 42, no. 1 (2019): 103-117. https://jos.ju-journal.org/jujs/article/view/31.

Afifi, Mahmoud, and Khaled F. Hussain. "The Achievement of Higher Flexibility in Multiple-Choice-Based Tests Using Image Classification Techniques." *International Journal on Document Analysis and Recognition (IJDAR)* (2019). https://doi.org/10.1007/s10032-019-00322-3.

Raundale, Pooja, Taruna Sharma, Saurabh Jadhav, and Rajan Margaye. "Optical Mark Recognition Using OpenCV." *International Journal of Computer Applications* 178, no. 37 (August 2019): 9.

Ware, Vidisha, Nithya Menon, Prajakti Varute, and Rachana Dhannawat. "Cost Effective Optical Mark Recognition Software for Educational Institutions." *International Journal of Advance Research, Ideas and Innovations in Technology* 5, no. 2 (2019): 1874.

Calado, M. P., A. A. Ramos, and P. Jonas. "An Application to Generate, Correct and Grade Multiple-Choice Tests." In *2019 6th International Conference on Systems and Informatics (ICSAI)*, 1548–1552. Shanghai, China, 2019. https://doi.org/10.1109/ICSAI48974.2019.9010132.

IAEME Publication. "Development of an Automated Test Item Analysis System with Optical Mark Recognition (OMR)." *IAEME Publication*, 2021. https://doi.org/10.34218/IJEET.12.1.2021.008.

Ascencio, H. E., C. F. Peña, K. R. Vásquez, M. Cardona, and S. Gutiérrez. "Automatic Multiple Choice Test Grader Using Computer Vision." In *2021 IEEE Mexican Humanitarian Technology Conference (MHTC)*, 65–72. Puebla, Mexico, 2021. https://doi.org/10.1109/MHTC52069.2021.9419920.

Kommey, Benjamin, Eliel Keelson, Frimpong, Seth Twum-Asare, Kwaku Konadu Akuffo. "Automatic Multiple Choice Examination Questions Marking and Grade Generator Software." *IPTEK The Journal for Technology and Science* 33, no. 3 (April 2023): 853-4098. https://doi.org/10.12962/j20882033.v33i3.14522.

Salih, Sardar. "Grading Multiple Choice Questions Based on Prepared Questions and Options Bookmarks in Bubble Sheet." *The Journal of Duhok University* 25, no. 2 (2022): 261-268. https://doi.org/10.26682/sjuod.2022.25.2.24.

Özcan, Fatih Taha, and Ayşe Eldem. "A New Application for Reading Optical Form with Standard Scanner by Using Image Processing Techniques." *MANAS Journal of Engineering* 11, no. 2 (December 2023): 252-260.

Authors Unknown. "Evaluating Scanned Paper Evaluation Sheets for Questions with the Choice of One Correct Answer from Several Offered." *IOP Conference Series: Materials Science and Engineering* 1298, no. 1 (December 2023): 012020. https://doi.org/10.1088/1757-899X/1298/1/01202.

Hafeez, Qamar, Waqar Aslam, Romana Aziz, and Ghadah Aldehim. "An Enhanced Fault Tolerance Algorithm for Optical Mark Recognition Using Smartphone Cameras." *IEEE Access* (2024): 1-1. https://doi.org/10.1109/ACCESS.2024.3451972.

Mahmud, S., et al. "Automatic Multiple Choice Question Evaluation Using Tesseract OCR and YOLOv8." In *2024 IEEE Conference on Artificial Intelligence (CAI)*, 246–252. Singapore, 2024. https://doi.org/10.1109/CAI59869.2024.00054.

AbhiSubrahmanyam, Kamuju, Mandava Sai Vineeth, Yalla Mani, Sai Suhith, Kaushik PrabhathBandi and Student. “An Optical Mark Recognition and Evaluation System Based On Image Processing.” (2022).

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Published

2025-03-30

How to Cite

Ali, E. R., & Shati, N. M. (2025). Evaluation System for Multiple-Choice Questions Using Optical Mark Recognition: A Survey. Journal of Al-Qadisiyah for Computer Science and Mathematics, 17(1), Comp. 202–213. https://doi.org/10.29304/jqcsm.2025.17.11975

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Section

Computer Articles