An Image Processing Oriented Optical Mark Recognition and Evaluation System
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Keywords:
Image Processing, Multiple Choice Test, OpenCV, Optical Mark Recognition, Python, QR CodeAbstract
In this study, a fast, reliable and cheap method is suggested for the recognition and evaluation of the marks of a multiple-choice test on the images that are obtained via the scanning of the optical forms printed on a standard sheet of paper with an ordinary scanner. This method is called the recognition of optical marks and is the process of capturing the data on the multiple-choice forms. The application of recognition has been developed by using the software language, Python and the image processing library, OpenCV. When the answer sheet is loaded in the application, incorrect answers are marked as red while the correct ones as green and with the calculation of the correct/incorrect answers and blanks, the result is printed on the optical form image. This method is economical, fast and quite successful. As a result of three examinations in Konya with the participation of 35.250 students, 105.750 optical forms were scanned with a scanner. The recognition success has been calculated as 99,76 %. The empirical studies have shown that the suggested system is more successful than the conventional optical mark recognition systems regarding accuracy, reliability and performance.
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G. Samtaş and M. Gülesin, "Sayısal Görüntü İşleme ve Farklı Alanlardaki Uygulamaları," Electronic Journal of Vocational Colleges, vol. 2, no. 1, pp. 85-97, 2011.
Parul, H. Monga, and M. Kaur, "A novel optical mark recognition technique based on biogeography based optimization," International Journal of Information Technology and Knowledge Management, vol. 5(2), pp. 331-333, 2012.
Anonymous. (2018, Erişim Tarihi: 29.8.2018). ICR, OCR and OMR - A Comparison of Technologies.
A. Yüksel, İ. Çankaya, M. Yalçınkaya, and N. Ateş, "Mobile based optical form evaluation system," Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi vol. 22, pp. 94 - 99, 2016.
S. B. Gaikwad, "Image Processing Based OMR Sheet Scanning," International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE), vol. 4, no. 3, pp. 519-522, 2015.
D. Patel and S. Zaid, "Efficient System For Evaluatıon Of Omr Sheet-A Survey," International Journal of Advanced Research in Engineering, Science & Management, vol. 3, no. 7, 2017.
G. Krishna, R. H. Ram, I. Madan, Kashif, and N. Sahu, "Implementation of OMR Technology with the Help of Ordinary Scanner," International Journal of Advanced Research in Computer Science and Software Engineering, vol. 3, no. 4, pp. 714-719, 2013.
I. A. Belag, Y. Gültepe, and T. M. Elmalti, "An Image Processing Based Optical Mark Recognition with the Help of Scanner," International Journal of Engineering Innovation & Research, vol. 7, no. 2, 2018.
Y. S. S. S. Reddy, A. S. Srinivas, and G. M. Krishna, "OMR Evaluation using Image Processing," International Journal of Innovations & Advancement in Computer Science, vol. 7, no. 4, 2018.
N. Kakade and R. C. Jaiswal, "OMR Sheet Evaluation Using Image Processing," Journal of Emerging Technologies and Innovative Research vol. 4, no. 12, pp. 640-643, 2017.
R. C. Gonzales and R. E. Woods, Digital Image Processing, 3 ed.: Prentice-Hall, Inc. Upper Saddle River, NJ, USA, 2002. [Online]. Available.
D. Karakuş, "Görüntü Analiz Yöntemleri İle Kayaçların Yapısal Özelliklerinin Tanımlanması," Doktora Tezi, Fen Bilimleri Enstitüsü, Dokuz Eylül Üniversitesi, 2006.
Ö. F. Boyraz and M. Z. Yıldız, "Mobil Damar Görüntüleme Cihaz Tasarımı," presented at the 4th International Symposium on Innovative Technologies in Engineering and Science - ISITES2016, (Alanya/Antalya - Turkey), 2016.
T. Helland. (2018, Erişim Tarihi : 07.09.2018). Seven grayscale conversion algorithms.
M. L. Mendelsohn and J. M. S. Prewitt, "The Analysıs of Cell Images," Annals of the New York Academy of Sciences, vol. 128, no. 3, pp. 1035-1053, 1966.
N. Otsu, "A Threshold Selection Method from Gray-Level Histograms," IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, no. 1, pp. 2 - 66, 1979.
N. Senthilkumaran and S. Vaithegi, "Image Segmentation By Using Thresholding Techniques For Medical Images," Computer Science & Engineering: An International Journal (CSEIJ), vol. 6, no. 1, pp. 1-13, 2016.
J. S. Weszka, R. N. Nagel, and A. Rosenfeld, "A Threshold Selection Technique," IEEE Transactions on Computers, vol. 23, no. 12, pp. 1322-1326, 1974.
R. M. Haralick, S. R. Sternberg, and X. Zhuang, "Image Analysis Using Mathematical Morphology," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 9, no. 4, pp. 532-550, 1987.
M. Sezgin and M. Sankur, "Survey over image thresholding techniques and quantitative performance evaluation," Journal of Electronic Imaging, vol. 13, no. 1, pp. 146-165, 2004.
C. Yu, C. Dian-ren, Y. Xu, and L. Yang, "Fast Two-Dimensional Otsu’s Thresholding Method Based on Integral Image " presented at the 2010 International Conference on Multimedia Technology (ICMT), Ningbo, China, 2010.
G. Bradski and A. Kaehler, Learning OpenCV. USA: O’Reilly Media, Inc., 2008.
S. Vijayarani and M. Vinupriya, "Performance Analysis of Canny and Sobel Edge Detection Algorithms in Image Mining," International Journal of Innovative Research in Computer and Communication Engineering, vol. 1, no. 8, pp. 1760-1767, 2013.
D. Auroux, L. D. Cohen, and M. Masmoudi, "Contour Detection and Completion for Inpainting and Segmentation Based on Topological Gradient and Fast Marching Algorithms," International Journal of Biomedical Imaging, vol. 2011, p. 20, 2011.
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