APPLICATION OF OPTICAL MARK RECOGNITION TECHNIQUES TO SURVEY ANSWER SHEETS AT DALAT UNIVERSITY
DOI:
https://doi.org/10.37569/DalatUniversity.11.1.791(2021)Keywords:
Computer vision, Image processing, Optical Mark Recognition (OMR), Survey answer sheet.Abstract
In this paper, we examine some image processing techniques used in optical mark recognition, and then we introduce an application that collects data automatically from survey answer sheets at Dalat University. This application is constructed with the Aforge framework. Two types of survey answer sheets are used as input forms for our application: the teaching quality and the administrative quality survey answer sheets. Results show that our application has good performance in recognizing handwritten marks, with an accuracy of 98.9% per 667 answer sheets. Moreover, this application is clearly a time-saving solution for administrative staff because the inputting process is now nine times faster than before.
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References
Abraham, S. (2020). Image manipulation: Filter and convolutions. https://www.cs.utexas.edu/~theshark/courses/cs324e/lectures/cs324e-6.pdf.
Bergeron, B. P. (1998). Optical mark recognition. Postgraduate Medicine, 104(2), 23-25.
Cip, P., & Horak, K. (2011). Concept for optical mark processing. Paper presented at the 22nd International DAAAM Symposium, Austria.
de Elias, E. M., Tasinaflfo, P. M., & Junio, R. H. (2019). Alignment, scale and skew correction for optical mark recognition documents based. Paper presented at the 2019 XV Workshop de Visão Computacional (WVC), Brazil.
Đỗ, N. T., & Phạm, V. B. (2007). Xử lý ảnh. Trường Đại học Thái Nguyên.
Kim, U. (2016). Phép tích chập trong xử lý ảnh (convolution). https:// www.stdio.vn/computer-vision/phep-tich-chap-trong-xu-ly-anh-convolution-r1vHu1
Kumar, S. (2015). A study on optical mark readers. International Interdisciplinary Research Journal, 3(11), 40-44.
Mai, H. A. (2014). Nghiên cứu ứng dụng kỹ thuật xử lý ảnh vào xử lý phiếu đánh giá môn học Trường Đại học Lâm nghiệp. Tạp chí Khoa học và Công nghệ Lâm nghiệp, (1), 141-146.
Ngô, Q. T., & Đỗ, N. T. (2000). Một số phương pháp nâng cao hiệu quả nhận dạng phiếu điều tra dạng dấu phục vụ cho thiết kế hệ nhập liệu tự động MarkRead. Tạp chí Tin học và Điều khiển học, 16(3), 65-73.
Phan, T. T. N., Nguyen, T. H. T., Nguyen, V. P., Thai, D. Q., & Vo, P. B. (2017). Vietnamese text extraction from book covers. Dalat University Journal of Science, 7(2), 142-152.
Popli, H., Parekh, H., & Sanghvi, J. (2014). Optical mark recognition. https://www.slideshare.net/HimanshuPopli/optical-mark-recognition-40292822
Sinha, U. (n.d). Image convolution examples. https://aishack.in/tutorials/image-convolution-examples.
Surbhi, G., Geetila, S., & Parvinder, S. S. (2012). A generalized approach to optical mark recognition. Paper presented at the International Conference on Computer and Communication Technologies (ICCCT'2012), Thailand.
Yang, Y. (2006). Image filtering: Noise removal, sharpening, and deblurring. http://eeweb.poly.edu/~yao/EE3414/image_filtering.pdf
Yunxia, J., Xichang, W., & Xichang, C. (2019). Research on OMR recognition based on convolutional neural network Tensorflow platform. Paper presented at the International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), China.
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Copyright (c) 2021 Thai Duy Quy, Phan Thi Thanh Nga, Nguyen Van Huy Dung.

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