A new application for reading optical form with standard scanner by using image processing techniques

Year 2023, Volume: 11 Issue: 1, 64 - 73, 01.07.2023

Fatih Taha Özcan , Ayşe Eldem

https://doi.org/10.51354/mjen.1217103

An Erratum to this article was published on December 25, 2023. https://dergipark.org.tr/en/pub/mjen/issue/81663/1409919

Abstract

Multiple choice exams appear as the most common method used to measure student success in education systems in Turkey and around the world. Evaluation of multiple choice exams is usually done by optical form reading systems using expensive optical forms and optical scanners. The most important reason for using optical form reading systems is to reduce the error rate to zero in the evaluation process. In this study, an alternative system is proposed for the evaluation of multiple choice exams. The designed system is a web-based software with high accuracy on evaluating that contains optical form design module, session planning module and evaluation module. The form in which the designed template will be printed on A4 paper instead of a special optical form and the system in which standard scanners will be used instead of the optical scanner, uses the image processing algorithms in the OpenCV library in the evaluation process which is an intermediate library developed in C#. The proposed system is coded to run in parallel to speed up the evaluation process. In order to determine the performance of the proposed system, the optical forms filled by 208 students studying at Karamanoğlu Mehmetbey University, Department of Computer Engineering were evaluated. The accuracy rate of the system has been determined as 99.97%. It has been determined that 1 optical evaluation time, calculated by dividing the total time obtained by running in parallel, by the number of evaluated optical forms, varies between 1.7 seconds and 15 seconds, depending on the scanning resolution.

Keywords

Image preprocessing techniques, multiple choice exam, optical form reading, segmentation, thresholding

References

• Temizkan, M. and M.E. Sallabaş, Okuduğunu Anlama Becerisinin Değerlendirilmesinde Çoktan Seçmeli Testlerle Açık Uçlu Yazılı Yoklamaların Karşılaştırılması. Dumlupınar Üniversitesi Sosyal Bilimler Dergisi, 2011(30): p. 207-220.
• Çakan, M., Geniş ölçekli başarı testlerinin eğitimindeki yeri ve önemi. Eğitim ve Bilim, 2003. 28(128).
• Nguyen, T.D., et al. Efficient and reliable camera based multiple-choice test grading system. 2011. IEEE.
• Aydemir, F., Standart Tarayıcılarla Web Tabanlı Bilgisayar Mühendisliği. 2012.
• Al-Marakeby, A., Multi Core Processors for Camera based OMR. International Journal of Computer Applications, 2013. 68(13): p. 1-5.
• Fisteus, J.A., A. Pardo, and N.F. García, Grading multiple choice exams with low-cost and portable computer- vision techniques. Journal of Science Education and Technology, 2013. 22(4): p. 560-571.
• Sattayakawee, N., Test scoring for non-optical grid answer sheet based on projection profile method. International Journal of Information and Education Technology, 2013. 3(2): p. 273-273.
• Şenol, M. and U. Fidan, C# ile Web Kameradan Optik Form Okuma. Afyon Kocatepe University Journal of Science & Engineering, 2014. 14(2).
• Patel, R., et al. CheckIt-A low cost mobile OMR system. 2015. IEEE.
• Hasan, R.H. and E.I.A. Kareem, An Image Processing Oriented Optical Mark Reader Based on Modify Multi- Connect Architecture MMCA. International Journal of Modern Trends in Engineering and Research (IJMTER), 2015. 2(7).
• Yüksel, A.S., et al., Mobile based optical form evaluation system. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 2016. 22(2): p. 94-99.
• Gyamfi, E.O. and Y.M. Missah, Pixel-Based Unsupervised Classification Approach for Information Detection on Optical Markup Recognition Sheet. Advances in Science, Technology and Engineering Systems Journal, 2017. 2(4): p. 121-132.
• Loke, S.C., K.A. Kasmiran, and S.A. Haron, A new method of mark detection for software-based optical mark recognition. Plos One, 2018. 13(11).
• Küçükkara, Z. and A.E. Tümer, An Image Processing Oriented Optical Mark Recognition and Evaluation System. International Journal of Applied Mathematics Electronics and Computers, 2018. 6(4): p. 59-64.
• Espitia, O., et al. Optical Mark Recognition Based on Image Processing Techniques for the Answer Sheets of the Colombian High-Stakes Tests. 2019. Springer.
• Afifi, M. and K.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. 22(2): p. 127-142.
• Ha, T. and N. Thu, An Application of Image Processing in Optical Mark Recognition. Vietnam Journal of Agricultural Sciences, 2020. 3(4 SE - ENGINEERING AND TECHNOLOGY).
• Çayıroğlu, İ., Resim Yumuşatma (Bulanıklaştırma-Blurrıng) Filtreleri. 2021.
• Ballard, D.H., Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition, 1981. 13(2): p. 111-122.
• Şaan, T.G., MPI İle Paralel Programlama. 2017.