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A segmentation method for examination paper questions

Published: 17 April 2024 Publication History

Abstract

This paper proposes a method for segmenting examination paper questions from the perspective of intelligent image processing. In terms of image preprocessing, firstly, the black area in the examination paper is extracted using the OTSU and adaptive threshold segmentation methods, followed by the extraction of edge contours using the sobel operator. To obtain the text and illustrations in the paper more accurately and completely, the contours are further enhanced using morphological operation to determine the areas containing the question items. Regarding the segmentation of the examination paper questions, the question areas are selected based on their aspect ratios, thus excluding the parts of option or illustration. Finally, a complete examination paper questions is determined by analyzing the top, bottom, left and right edge of the two adjacent questions areas, each complete question is segmented and displayed using rectangular bounding boxes.

References

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Hu Xiang, 2019, “Design and Implementation of Examination Paper Intelligent Assistant Marking System”, Huazhong University of Science and Technology.
[2]
Guo Leibin, 2020, “A Research and Implementation of Mathematical Papers’ Layout Segmentation Algorithm”, University of Electronic Science and Technology.
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Ji He, Chen Yajun, Liu Xue, Ma Deng, 2022, “Design of drug box detection system based on machine vision”, Wireless Internet Technology,19(06):76-77.
[4]
ZHONG Qiao, 2017, “Text Line Segmentation and Correction on Scanned Image based on Graph Theory”, Hunan University.
[5]
Zhang Jiaying, 2019. “Design of an intelligent recognition system for test papers based on machine vision” [J]. electronic production,2019(14):22-24.
[6]
ZHANG Yi, KUANG Yi, WANG Mei, HUANG Zhi−yuan, HU Song, 2020, “Human Contour Detection Algorithm Based on OpenCV”, Computer technology and development, 2020, 30(08).
[7]
Muthukrishnan.R, M.Radha, 2011, “Edge Detection Techniques For Image Segmentation”, International Journal of Computer Science and Information Technology 3(6):259-267.
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LEI De-chao, REN Shou-hua, 2022 “Analysis and Research of License Plate Recognition System Based on OpenCV Image Processing”, College of information and electrical engineering, Heilongjiang Bayi Agricultural University,30(04).
[9]
Mohammad Hosein Jafari, Shadrokh Samavi, 2015, “Iterative Semi-Supervised Learning Approach for Color Image Segmentation”, 9th Iranian Conference on Machine Vision and Image Processing.
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GONG Jiamin, ZHAO Mengkai, SUN Yibin, JIANG Jiewei, 2022, “ZHANG Kaize Test question automatic segmentation method based on named entity recognition”, Transducer and Microsystem Technologies, 2022.

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EITCE '23: Proceedings of the 2023 7th International Conference on Electronic Information Technology and Computer Engineering
October 2023
1809 pages
ISBN:9798400708305
DOI:10.1145/3650400
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 April 2024

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  • Research-article
  • Research
  • Refereed limited

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  • Student Innovation and Entrepreneurship Project of Xiamen University Tan Kah Kee College:Design of Deep Learning-Based Intelligent test paper processing

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EITCE 2023

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Overall Acceptance Rate 508 of 972 submissions, 52%

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