Optical Mark Recognition Evaluation System using Dual-Component Approach

Authors

  • R. C. Dharmik Assistant Professor Department of Information Technology, Yeshwantrao Chavan College of Engineering ,Nagpur, India
  • Sudhir Rangari Assistant Professor Department of Information Technology JSPM’S Jayawantrao Sawant College of Engineering, Hadapsar, Pune.
  • Sachin Jain Assistant Professor Department of Computer Science, Oklahoma State University Stillwater, United States.
  • Ajinkya Nilawar Assistant Professor Department of Electronics and Communication Engineering, Shri Ramdeobaba College of Engineering and Management, Maharashtra (India)
  • Gopal Deshmukh Assistant Professor Department of Computer Engineering VIIT, Pune, INDIA
  • Abhay Yeole Assistant Professor Department of Information Technology, Yeshwantrao Chavan College of Engineering ,Nagpur, India

Keywords:

OMR, Mark detection, Automation, Integration

Abstract

The Optical Mark Recognition (OMR) Project represents a comprehensive effort to leverage advanced technology for automating the data capture and processing of paper based forms. In response to the increasing need for efficient and accurate data handling, this project introduces an OMR system designed to alleviate the challenges associated with manual data entry and processing. The core objectives include developing both hardware and software components capable of accurately interpreting user-marked responses on predefined areas of forms, such as surveys, exams, and questionnaires. The OMR system surpasses expectations in processing speed, boasting an average processing time of [insert time, e.g., seconds per sheet]. This efficiency positions the system as an ideal solution for high-throughput scenarios, including large-scale examinations or surveys. The project's scope extends to various sectors, including education, healthcare, market research, and government, where large volumes of data must be collected and analyzed expeditiously. The significance of OMR technology lies in its ability to enhance speed, accuracy, and reliability in data processing. By automating traditionally labor-intensive tasks, the project aims to improve overall productivity, reduce costs, and minimize errors inherent in manual data processing methods. .The methodology adopted for the OMR project involves the integration of optical scanners for physical data capture and sophisticated software algorithms for image processing, mark detection, and data extraction. This dual-component approach ensures a seamless and efficient OMR system capable of handling diverse forms.

Downloads

Download data is not yet available.

References

S. Maniar, J. Parmani, M. Bodke and K. Saxena, "Generation and grading of arduous MCQs using NLP and OMR detection using OpenCV," 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), Kharagpur, India, 2021

N. Kowsalya, S. Lavanya, S. M. Raja N. and S. Padmapriya, "Diagnosis of schizophrenic brain MRI images using Level- Set Evolution," 2020 International Conference on System, Computation, Automation and Networking (ICSCAN), Pondicherry, India, 2020

Optical Mark Recognition using Open CV{tag} {/tag}International Journal of Computer Applications Foundation of Computer Science (FCS), NY, US PoojaRaundale, Taruna Sharma, SaurabhJadhav, RajanMargaye. Year of publication 2019.

Astha Gupta, SandhyaAvasthi (7, July 2016 ) Image-based low-cost method to the OMR process for surveys and research, International Journal of Scientific Engineering and Applied Science (IJSEAS) – Volume-2: www.ijseas.com.

P. Sanguansat, Robust and low-cost Optical Mark Recognition for automated data entry, 2015 12th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTICON), 2015.

Webcam Based Real-Time Robust Optical Mark Recognition HuseyinAtasoy, EsenYildirim, +1 author KadirTohma Published in ICONIP 9 November 2015 Computer Science.

R. S, K. Atal, and A. Arora Cost Effective Optical Mark Reader, International Journal of Computer Science and Artificial Intelligence, vol. 3, no. 2, pp. 4449, Jul. 2013.

R. Patel, S. Sanghavi, D. Gupta and M. S. Raval, "CheckIt - A low cost mobile OMR system," TENCON 2015 - 2015 IEEE Region 10 Conference, Macao, China, 2015

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 ,2012.

Anonymous. (2018, ErişimTarihi: 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," PamukkaleÜniversitesiMühendislikBilimleriDergisi 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. 30

Ms.Sumitra B. Gaikwad, “Image Processing based OMR Sheet Scanning,” International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE) Volume 4, Issue 3, March 2015.

John D. "OMR." A Dictionary of Computing 2004. Retrieved March 17, 2011 from Encyclopedia.comhttp://www.encyclopedia.com/doc/IOII-OMR.html

Rusul Hussein Hasan, Emad I Abdul 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) Volume 02, Issue 07, [July– 2015].

Downloads

Published

07.01.2024

How to Cite

Dharmik, R. C. ., Rangari, S. ., Jain, S. ., Nilawar, A. ., Deshmukh, G. ., & Yeole, A. . (2024). Optical Mark Recognition Evaluation System using Dual-Component Approach. International Journal of Intelligent Systems and Applications in Engineering, 12(10s), 349–353. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4383

Issue

Section

Research Article

Most read articles by the same author(s)