Research Article
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An Image Processing Oriented Optical Mark Recognition and Evaluation System

Year 2018, Volume: 6 Issue: 4, 59 - 64, 31.12.2018

Zeki Küçükkara Abdullah Erdal Tümer

Abstract

Standart bir kağıt üzerine basılmış optik formların,
sıradan bir tarayıcı ile taratılması sonucunda elde edilen görüntüler üzerindeki
çoktan seçmeli test işaretlemelerinin tanınması ve değerlendirilmesi için bir yöntem. Bu yöntem optik işaret
tanıma/okuma olarak adlandırılır ve işaretlenmiş çoktan seçmeli formlardaki verilerin
yakalanması işlemidir. Optik işaret tanıma/okuma uygulaması Python yazılım dili
ve OpenCV görüntü işleme kütüphanesi kullanılarak geliştirilmiştir. Uygulama içerisine
cevap anahtarının girilmesi ile yanlış işaretlenen daire kırmızı, doğru
cevaplanan daire yeşil olarak işaretlenmiş, doğru/yanlış/boş bilgiler
hesaplanarak optik form görüntüsü üzerine yazdırılmıştır. 

Keywords

Image Processing Multiple Choice Test OpenCV Optical Mark Recognition Python QR Code

References

  • 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.
  • [10] 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.
  • [21] 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.

Year 2018, Volume: 6 Issue: 4, 59 - 64, 31.12.2018

Zeki Küçükkara Abdullah Erdal Tümer

Abstract

References

  • 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.
  • [10] 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.
  • [21] 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.
There are 24 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Article
Authors

Zeki Küçükkara 0000-0002-5204-0819

Abdullah Erdal Tümer This is me 0000-0001-7747-9441

Publication Date December 31, 2018
Published in Issue Year 2018 Volume: 6 Issue: 4

Cite

APA Küçükkara, Z., & Tümer, A. E. (2018). An Image Processing Oriented Optical Mark Recognition and Evaluation System. International Journal of Applied Mathematics Electronics and Computers, 6(4), 59-64.
AMA Küçükkara Z, Tümer AE. An Image Processing Oriented Optical Mark Recognition and Evaluation System. International Journal of Applied Mathematics Electronics and Computers. December 2018;6(4):59-64.
Chicago Küçükkara, Zeki, and Abdullah Erdal Tümer. “An Image Processing Oriented Optical Mark Recognition and Evaluation System”. International Journal of Applied Mathematics Electronics and Computers 6, no. 4 (December 2018): 59-64.
EndNote Küçükkara Z, Tümer AE (December 1, 2018) An Image Processing Oriented Optical Mark Recognition and Evaluation System. International Journal of Applied Mathematics Electronics and Computers 6 4 59–64.
IEEE Z. Küçükkara and A. E. Tümer, “An Image Processing Oriented Optical Mark Recognition and Evaluation System”, International Journal of Applied Mathematics Electronics and Computers, vol. 6, no. 4, pp. 59–64, 2018.
ISNAD Küçükkara, Zeki - Tümer, Abdullah Erdal. “An Image Processing Oriented Optical Mark Recognition and Evaluation System”. International Journal of Applied Mathematics Electronics and Computers 6/4 (December 2018), 59-64.
JAMA Küçükkara Z, Tümer AE. An Image Processing Oriented Optical Mark Recognition and Evaluation System. International Journal of Applied Mathematics Electronics and Computers. 2018;6:59–64.
MLA Küçükkara, Zeki and Abdullah Erdal Tümer. “An Image Processing Oriented Optical Mark Recognition and Evaluation System”. International Journal of Applied Mathematics Electronics and Computers, vol. 6, no. 4, 2018, pp. 59-64.
Vancouver Küçükkara Z, Tümer AE. An Image Processing Oriented Optical Mark Recognition and Evaluation System. International Journal of Applied Mathematics Electronics and Computers. 2018;6(4):59-64.
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