COMPUTER SYSTEM FOR AUTOMATED PROCESSING OF THE RESULTS OF THE TEST EXAMS - Problems of Information Technology

COMPUTER SYSTEM FOR AUTOMATED PROCESSING OF THE RESULTS OF THE TEST EXAMS - Problems of Information Technology

COMPUTER SYSTEM FOR AUTOMATED PROCESSING OF THE RESULTS OF THE TEST EXAMS - Problems of Information Technology

COMPUTER SYSTEM FOR AUTOMATED PROCESSING OF THE RESULTS OF THE TEST EXAMS - Problems of Information Technology

COMPUTER SYSTEM FOR AUTOMATED PROCESSING OF THE RESULTS OF THE TEST EXAMS - Problems of Information Technology
COMPUTER SYSTEM FOR AUTOMATED PROCESSING OF THE RESULTS OF THE TEST EXAMS - Problems of Information Technology
AZERBAIJAN NATIONAL ACADEMY OF SCIENCES

№2, 2020

COMPUTER SYSTEM FOR AUTOMATED PROCESSING OF THE RESULTS OF THE TEST EXAMS

Kamil R.Aida-zade, Chingiz J. Khalilov, Elshan E. Mustafayev, Ilgar M. Mahmudov

Pattern recognition is increasingly used in large information systems. The development of the theoretical base of image processing and the widespread use of free open source libraries make it possible to use new solutions in a variety applied problems. One of these tasks is the automatic machine processing of the answers of massively conducted test exams. This paper describes the developed system, used in processing the results of mass exams, which shows the ability for reliable, rapid and objective assessment. This system can be configured on almost any type of form. Its use also allows to abandon the expensive and difficult to use OMR scanners. To increase productivity of system we propose to use the multicore/multithreading property of modern processors to parallelize processes within a single workstation. As a result of experiments, it is found that the transition to multi-threaded recognition can increase productivity up to 3.5 times compared to single-threaded (pp.32-40).

Keywords: information processing, intelligent system, evaluation of results, image processing, multithreading recognition, automatic response processing.
DOI : 10.25045/jpit.v11.i2.03
References
  • Optical mark recognition. https://en.wikipedia.org/wiki/Optical_mark_recognition
  • ABBYY FormReader Enterprise Edition. User`s Guide. 2016.
  • Bhatia E.N. Optical Character Recognition Techniques: A Review // International Journal of Advanced Research in Computer Science and Software Engineering, vol.4, issue 5, May 2014, pp.1219–1223.
  • Hamad K.A., Kaya M. A Detailed Analysis of Optical Character Recognition Technology // International Journal of Applied Mathematics, Electronics and Computers, 2016, 4 (Special Issue), pp.244–249.
  • Мустафаев Э.Э. Многоуровневая иерархическая система распознавания рукописных форм / Материалы научной конференции «Современные проблемы прикладной математики», Баку, 2002, с.154–157.
  • Айда-заде К.Р., Талыбов С.Г., Мустафаев Э.Э. Многоуров­невая система распознавания рукописных форм / Доклады 11-й Всероссийской конференции «Математические методы распознавания образов», Москва, 2003, с.230–233.
  • Monga P.H., Kaur M. A Novel Optical Mark Recognition Technique Based on Biogeography Based Optimization // International Journal of Information Technology and Knowledge Management, July-December 2012, vol.5, no.2, pp.331–333.
  • Rakesh S, Kailash Atal, Ashish Arora. Cost Effective Optical Mark Reader // International Journal of Computer Science and Artificial Intelligence Jun. 2013, vol.3, issue 2, pp.44–49.
  • LeCun Y., Doser B., Denker J. et al. Handwritten Digit Recognition with a Back-Propagation Network // Advances in Neural Information Processing Systems, D.S.Touretzky, Ed., Denver, 1990, vol.2, pp.396–404.
  • LeCun Y., Bottou L., Orr G., and Muller K. Efficient BackProp // Neural Networks: Tricks of the Trade, Germany, Berlin:Springer, 2012, pp.9–48.
  • LeCun Y., Denker J., Solla S. Optimal Brain Damage // Advances in Neural Information Processing Systems 2, 1990, pp.598–605.
  • Айда-заде К.Р, Мустафаев Э.Э. Ассоциативные многоуров­невые системы распознавания объектов // Известия НАН Азербайджана, серия ф.т. и м.н., 2001, №3, с.15–18.
  • Айда-заде К.Р., Мустафаев Э.Э. Повышение интеллек­туального уровня искусственных нейронных сетей // Известия НАН Азербайджана, серия ф.т. и м.н., 2003, №3, с.17–20.
  • Mori M., Wakahara T., Ogura K. Measures for Structural and Global Shape Description in Handwritten Kanji Character Recognition / Proc. SPIE 3305, Document Recognition V, 1 April 1998, pp.81–89.
  • Hwang Y.S., Bang S.Y. Recognition of Unconstrained Handwritten Numerals by Radial Basis Function Network Classifier // Pattern Recognition Letters, 1997, vol.18, pp.657–664.
  • Thomas H. Cormen, Charles E. Leiserson, Rjnald L. Rivest. Clifford Stein. Introduction to Algorithms. M: Publishing house “Williams”, 2014.