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Optical Mark Recognition: Advances, Difficulties, and Limitations

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Abstract

Performing mass assessment corrections is a tedious and costly task, especially when allocating teachers or instructors to do these corrections. Such task can be facilitated and accelerated by Optical Mark Recognition (OMR) technology, bringing educational institutions to look for this solution. OMR initially appeared as a dedicated hardware solution, but software solutions have emerged with the evolution of technology, gradually replacing dedicated equipment. However, most solutions lack flexibility, mainly for the end-users. The literature proposes several methods, often highlighting the issue of cost and accessibility. The present work reviews 35 papers around OMR subject and lists the reviewed methods’ main characteristics, datasets, restrictions, technological challenges, techniques used, processing time, and accuracy. We map and categorize the restrictions to help the reader improve the current software OMR technology state. We also call the community’s attention to the lack of a standard dataset that could be used to compare OMR solutions.

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References

  1. Afifi M, Hussain KF. The achievement of higher flexibility in multiple-choice-based tests using image classification techniques. Int J Doc Anal Recognit (IJDAR). 2019;22(2):127–42. https://doi.org/10.1007/s10032-019-00322-3.

    Article  Google Scholar 

  2. AL-Marakeby A. Multi core processors for camera based OMR. Int J Comput Appl. 2013;68(13):1–5. https://doi.org/10.5120/11636-7116.

    Article  Google Scholar 

  3. Binmakhashen GM, Mahmoud SA. Document layout analysis: a comprehensive survey. ACM Comput Surv. 2020;52(6):1–36. https://doi.org/10.1145/3355610.

    Article  Google Scholar 

  4. Booka M, Oku H, Scheller A, Yamaoka S. Usability of Optical Mark Reader sheet as an answering tool in testing. Stud Health Technol Inform. 2017;242:939–46.

    Google Scholar 

  5. Burger W, Burge MJ. Digital image processing: an algorithmic introduction using java. Texts in computer science. London: Springer; 2016. https://doi.org/10.1007/978-1-4471-6684-9.

    Book  Google Scholar 

  6. Calado MP, Ramos AA, Jonas P. An application to generate, correct and grade multiple-choice tests. In: 2019 6th International Conference on Systems and Informatics (ICSAI), Shanghai, China: IEEE; 2019. pp. 1548–1552. https://doi.org/10.1109/ICSAI48974.2019.9010132.

  7. Catalan JA. A framework for automated multiple-choice exam scoring with digital image and assorted processing using readily available software. In: DLSU Research Congress Proceedings, Philippines: Manila; 2017. p 5.

  8. Chai D. Automated marking of printed multiple choice answer sheets. In: 2016 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE), Bangkok, Thailand: IEEE; 2016. pp. 145–149. https://doi.org/10.1109/TALE.2016.7851785.

  9. Dayananda Kumar NC, Suresh KV, Dinesh R. Automated parameter-less Optical Mark Recognition. In: Data Analytics and Learning, Singapore: Springer Singapore; 2019. pp. 185–195.

  10. de Elias EM, Tasinaffo PM, Hirata R. Alignment, scale and skew correction for Optical Mark Recognition documents based. In: 2019 XV Workshop de Visão Computacional (WVC), Brazil: IEEE, São Bernardo do Campo. 2019; pp. 26–31. https://doi.org/10.1109/WVC.2019.8876933.

  11. de Elias EM, Fogaça LR, Milani V, Forster CHQ. Classification techniques for test answer fields in digitized documents. In: Proceedings of The 21st World Muilti-Conference on Systemics, Cybernetics and Informatics (WMSCI 2017), Florida, USA; 2017. pp. 221–226.

  12. Gaikwad SB. Image processing based OMR sheet scanning. Int J Adv Res Electron Commun Eng. 2015;4(3):4.

    Google Scholar 

  13. Goodfellow I, Bengio Y, Courville A. Deep learning. Adaptive computation and machine learning. Cambridge: The MIT Press; 2016.

    MATH  Google Scholar 

  14. Greenfield E. OMR scanners: reflective technology makes the difference. Technol Horizons Educ 1991;18(7):8(3).

  15. Hadi YR, Karim F, Mohammad Hadi A. A novel approach of skew estimation and correction in persian manuscript text using radon transform. In: 2012 IEEE Symposium on Computers & Informatics (ISCI). Penang, Malaysia: IEEE. 2012; pp. 198–202. https://doi.org/10.1109/ISCI.2012.6222694.

  16. Higgins C. Pencils down: scantron inventor Michael Sokolski has died. 2012. https://www.mentalfloss.com/article/31057/pencils-down-scantron-inventor-michael-sokolski-has-died. Accessed 30 Jun 2021.

  17. Hussmann S, Deng PW. A high-speed optical mark reader hardware implementation at low cost using programmable logic. Real-Time Imaging. 2005;11(1):19–30. https://doi.org/10.1016/j.rti.2005.03.001.

    Article  Google Scholar 

  18. Hutchison LAD, Barrett WA. Fast registration of tabular document images using the Fourier-Mellin transform. In: First International Workshop on Document Image Analysis for Libraries, 2004. Proceedings., Palo Alto, CA, USA: IEEE; 2004. pp. 253–267, https://doi.org/10.1109/DIAL.2004.1263254.

  19. Automated test scoring. IBM Corporation. https://www.ibm.com/ibm/history/ibm100/us/en/icons/testscore. Accessed 30 Jun 2021.

  20. Jaiswal P. Integrating educational technologies to augment learners’ academic achievements. Int J Emerg Technol Learn (iJET). 2020;15(02):145. https://doi.org/10.3991/ijet.v15i02.11809.

    Article  Google Scholar 

  21. Ju Y, Wang X, Chen X. Research on OMR recognition based on convolutional neural network tensorflow platform. In: 2019 11th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), Qiqihar, China: IEEE; 2019. pp. 688–691, https://doi.org/10.1109/ICMTMA.2019.00157.

  22. Karunanayake N. OMR sheet evaluation by web camera using template matching approach. Int J Res Emerg Sci Technol. 2015;2(8):40–4.

    Google Scholar 

  23. Khan I, Rahman S, Alam F. An efficient, cost effective and user friendly approach for MCQs treatment. Proc Pakistan Acad Sci Phys Comput Sci. 2018;55(4):39–44 (number: 4).

    Google Scholar 

  24. Krishna G, Rana HR, Madan I, Sahu N. Implementation of OMR technology with the help of ordinary scanner. Int J Adv Res Comput Sci Softw Eng. 2013;3(4):714–9.

    Google Scholar 

  25. Loke SC, Kasmiran KA, Haron SA. A new method of mark detection for software-based optical mark recognition. PLoS One. 2018;13(11):e0206420. https://doi.org/10.1371/journal.pone.0206420.

    Article  Google Scholar 

  26. Mahajan NV, Apoorva KJ. Various skew detection and correction techniques: a survey. Int J Adv Res Comput Sci Softw Eng. 2015;5(3):336–9.

    Google Scholar 

  27. Mehta N, Doshi J. A review of handwritten character recognition. Int J Comput Appl. 2017;165(4):37–40. https://doi.org/10.5120/ijca2017913855.

    Article  Google Scholar 

  28. Nguyen TD, Hoang Manh Q, Bui Minh P, Nguyen Thanh L, Hoang TM. Efficient and reliable camera based multiple-choice test grading system. In: The 2011 International Conference on Advanced Technologies for Communications (ATC 2011), Da Nang, Vietnam: IEEE; 2011. pp. 268–271, https://doi.org/10.1109/ATC.2011.6027482.

  29. Papandreou A, Gatos B, Perantonis SJ, Gerardis I. Efficient skew detection of printed document images based on novel combination of enhanced profiles. Int J Doc Anal Recognit (IJDAR). 2014;17(4):433–54. https://doi.org/10.1007/s10032-014-0228-5.

    Article  Google Scholar 

  30. Patel NV, Prajapati GI, Bharuch S. Various techniques for assessment of OMR sheets through ordinary 2D scanner: a survey. Int J Eng Res. 2015. https://doi.org/10.17577/IJERTV4IS090675.

    Article  Google Scholar 

  31. Patel R, Sanghavi S, Gupta D, Raval MS. CheckIt—a low cost mobile OMR system. In: TENCON 2015–2015 IEEE Region 10 Conference, Macao; IEEE; 2015. pp. 1–5, https://doi.org/10.1109/TENCON.2015.7372983.

  32. Pérez-Benedito JL, Aragón EQ, Alriols JA, Medic L. Optical Mark Recognition in student continuous assessment. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje. 2014;9(4):133–8. https://doi.org/10.1109/RITA.2014.2363005.

    Article  Google Scholar 

  33. Rachchh K, Gopi ES. Inclusion of vertical bar in the OMR sheet for image-based robust and fast OMR evaluation technique using mobile phone camera. In: Kulkarni AJ, Satapathy SC, Kang T, Kashan AH, editors. Proceedings of the 2nd International Conference on Data Engineering and Communication Technology, vol. 828. Advances in intelligent systems and computing. Singapore: Springer; 2019. p. 39–46.

    Chapter  Google Scholar 

  34. Rasiq GRI, Al Sefat A, Hasnain MF. Mobile based MCQ answer sheet analysis and evaluation application. In: 2019 8th International Conference System Modeling and Advancement in Research Trends (SMART). Moradabad, India: IEEE. 2019; pp. 144–7. https://doi.org/10.1109/SMART46866.2019.9117468.

  35. Reddy YSSS, Srinivas AS, Krishna GM. OMR evaluation using image processing. Int J Innov Adv Comput Sci. 2018;7(4):572–6.

    Google Scholar 

  36. Sanguansat P. Robust and low-cost Optical Mark Recognition for automated data entry. In: 2015 12th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), Hua Hin, Cha-am, Thailand: IEEE; 2015. pp. 1–5, https://doi.org/10.1109/ECTICon.2015.7206937.

  37. Shivanna R, Atal K, Arora A. Cost effective Optical Mark Reader. Int J Comput Sci Artif Intell. 2013;3(2):44–9. https://doi.org/10.5963/IJCSAI0302002.

    Article  Google Scholar 

  38. Singh C, Bhatia N, Kaur A. Hough transform based fast skew detection and accurate skew correction methods. Pattern Recogn. 2008;41(12):3528–46. https://doi.org/10.1016/j.patcog.2008.06.002.

    Article  MATH  Google Scholar 

  39. Smith AM. Optical mark reading - making it easy for users. In: Proceedings of the 9th annual ACM SIGUCCS conference on User services - SIGUCCS ’81, Atlanta, Georgia, United States: ACM Press; 1981. pp. 257–263, https://doi.org/10.1145/800079.802600.

  40. Talib A, Ahmad N, Tahar W. OMR form inspection by web camera using shape-based matching approach. Int J Eng Res. 2015;3(4):29–35.

  41. Tjahyadi H, Lukas S, Albert S, Krisnadi D. automated scoring of multiple-choice test using template matching technique. In: Proceedings of the 2nd International Symposium on Computer Science and Intelligent Control - ISCSIC ’18, Stockholm, Sweden: ACM Press; 2018. pp. 1–5, https://doi.org/10.1145/3284557.3284702.

  42. Ware V, Menon N, Varute P, Dhannawat RA. Cost effective optical mark recognition software for educational institutions. Int J Adv Res Ideas Innov Technol. 2019;5(2):1874–7.

    Google Scholar 

  43. Weaver R, Chalkley B. Introducing objective tests and OMR-based student assessment: a case study. J Geogr Higher Educ. 1997;21(1):114–21. https://doi.org/10.1080/03098269708725416.

    Article  Google Scholar 

  44. Wynn R. Optical Mark Recognition. Data Process. 1984;26(9):26–7. https://doi.org/10.1016/0011-684X(84)90434-9.

    Article  Google Scholar 

  45. Zampirolli FdA, Batista VR, Quilici-Gonzalez JA. An automatic generator and corrector of multiple choice tests with random answer keys. In: 2016 IEEE Frontiers in Education Conference (FIE). Erie, PA, USA: IEEE. 2016; pp. 1–8. https://doi.org/10.1109/FIE.2016.7757467.

  46. Zampirolli FdA, Gonzalez JAQ. Automatic correction of multiple-choice tests using digital cameras and image processing. In: IX Workshop de Visão Computacional (WVC 2013), Rio de Janeiro: Botafogo; 2013. p. 6.

  47. Zhang L, Li B, Zhang Q, Hsiao IH. Does a distributed practice strategy for multiple choice questions help novices learn programming? Int J Emerg Technol Learn (iJET). 2020;15(18):234. https://doi.org/10.3991/ijet.v15i18.10567.

    Article  Google Scholar 

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Funding

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.

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Authors and Affiliations

  1. Aeronautics Institute of Technology - ITA, São José dos Campos, Brazil

    Erik Miguel de Elias & Paulo Marcelo Tasinaffo

  2. Institute of Mathematics and Statistics, University of São Paulo - USP, São Paulo, Brazil

    R. Hirata Jr.

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  1. Erik Miguel de Elias
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  2. Paulo Marcelo Tasinaffo
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  3. R. Hirata Jr.
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Correspondence to Erik Miguel de Elias.

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de Elias, E.M., Tasinaffo, P.M. & Hirata, R. Optical Mark Recognition: Advances, Difficulties, and Limitations. SN COMPUT. SCI. 2, 367 (2021). https://doi.org/10.1007/s42979-021-00760-z

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