[1]张 瑞,张希厚,曹嘉玲,等.基于图像处理和深度学习的题库生成系统[J].计算机技术与发展,2021,31(04):142-146.[doi:10. 3969 / j. issn. 1673-629X. 2021. 04. 024]
　ZHANG Rui,ZHANG Xi-hou,CAO Jia-ling,et al.A Question Bank Generation System Based on ImageProcessing and Deep Learning[J].,2021,31(04):142-146.[doi:10. 3969 / j. issn. 1673-629X. 2021. 04. 024]
点击复制

基于图像处理和深度学习的题库生成系统()

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
31
期数:
2021年04期
页码:
142-146
栏目:
应用前沿与综合
出版日期:
2021-04-10

文章信息/Info

Title:
A Question Bank Generation System Based on ImageProcessing and Deep Learning
文章编号:
1673-629X(2021)04-0142-05
作者:
张 瑞; 张希厚; 曹嘉玲; 罗彬杰; 万永菁
华东理工大学,上海 200237
Author(s):
ZHANG Rui; ZHANG Xi-hou; CAO Jia-ling; LUO Bin-jie; WAN Yong-jing
East China University of Science and Technology,Shanghai 200237,China
关键词:
图像处理; 深度学习; 残差神经网络; 题目切割; 智能化题库
Keywords:
image processing; deep learning; ResNet; question segmentation; intelligent question bank
分类号:
TP751. 1
DOI:
10. 3969 / j. issn. 1673-629X. 2021. 04. 024
摘要:
随着学生的学业压力不断加重,学生在复习时需要消化整理大量做过的试卷,传统的方法是手抄或剪贴题目,耗时耗力。 为了帮助学生更高效便捷地整理旧试卷,研究了如何对一张批改后的试卷进行自动处理,同时生成题库的方法。由学生在题号处画圈作为标记,首先通过图像处理技术,在 HSV 颜色空间获得红色的题号标记和批改痕迹, 根据题号对试卷题目和批改痕迹进行分割,得到每道题的批改痕迹和题干,再利用 ResNet 深度学习网络判断批改痕迹的类型,最后依此将题目分类保存。 为了验证算法的有效性,选定小学数学试卷作为测试样本。 实验结果表明,该系统对测试样卷有较好的分割效果,能够较为清晰地将题目与批改痕迹进行切割;同时可以相对准确地判断批改类型,实现了高效的分类题库生成。
Abstract:
As the academic pressure of students increase, students need to digest and sort out a large number of done papers when reviewing. Traditional way is to transcribe or? cut the papers and paste. In order to help students sort out the written test papers more effi鄄ciently and conveniently,we study how to automatically process a corrected paper and generate a question bank. The papers are already marked by students. Through image processing technology, the red marks at the question number and correction marks are separated from the whole papers in HSV color space, and thus the questions and correction marks are automatically divided according to the question number,and ResNet, the deep learning network, is used to judge the type of correction marks, according to which the questions are classified and saved. To testify the efficiency of arithmetic,we test on primary school math papers. Experiment shows that this system has an ideal segmentation effect on the test sample and? can cut down the question and correction marks clearly. At the same time,the cor鄄rection type can be judged relatively accurately,and the efficient classification database generation can be realized.

相似文献/References:

[1]李雷 张建民.一种改善的基于支持向量机的边缘检测算子[J].计算机技术与发展,2010,(03):125.
　LI Lei,ZHANG Jian-min.An Improved Edge Detector Using the Support Vector Machines[J].,2010,(04):125.
[2]张艳丽 保文星.粒子群优化算法在图像边缘检测中的研究应用[J].计算机技术与发展,2009,(05):26.
　ZHANG Yan-li,BAO Wen-xing.Research and Application of Image Edge Detection Based on PSO Algorithm[J].,2009,(04):26.
[3]詹金兰 李翠华.模拟实验系统的可视化研究[J].计算机技术与发展,2009,(05):228.
　ZHAN Jin-lan,LI Cui-hua.Visualization Research on Simulation Experiment System[J].,2009,(04):228.
[4]张家栋 张强 霍凯.图像处理在轴承荧光磁粉探伤中的应用研究[J].计算机技术与发展,2009,(08):216.
　ZHANG Jia-dong,ZHANG Qiang,HUO Kai.Study on Application of Image Processing in Bearing Fluorescent Magnetic Detection[J].,2009,(04):216.
[5]王文豪 张亚红 朱全银 单劲松.QR Code二维条形码的图像识别[J].计算机技术与发展,2009,(10):123.
　WANG Wen-hao,ZHANG Ya-hong,ZHU Quan-yin,et al.Image Recognition in 2 - D Bar Code Based on QR Code[J].,2009,(04):123.
[6]李孟歆 吴成东.粗糙集理论在图像处理中的应用[J].计算机技术与发展,2009,(03):208.
　LI Meng-xin,WU Cheng-dong.Rough Set Theory and Its Applications in Image Processing[J].,2009,(04):208.
[7]武彬.一种离焦模糊图像的复原方法[J].计算机技术与发展,2008,(01):74.
　WU Bin.A Method of Defocus Blurred Image Restoration[J].,2008,(04):74.
[8]蒋恩松 肖辉军 孙刘杰 熊清廉.基于机器视觉的套印误差自动检测系统设计[J].计算机技术与发展,2008,(07):173.
　JIANG En-song,XIAO Hui-jun,SUN Liu-jie,et al.Design of Automatic Detecting Printing Registration Deviation System Based on Machine Vision[J].,2008,(04):173.
[9]汪继文 林胜华 沈玉峰 邱剑锋.一种基于各向异性扩散的图像处理方法[J].计算机技术与发展,2008,(08):98.
　WANG Ji-wen,LIN Sheng-hua,SHEN Yu-feng,et al.An Approach for Image Restoration Based on Anisotropic Diffusion[J].,2008,(04):98.
[10]余志强 戎蒙恬 袁丹寿.一种用单端口SRAM构成的FIFO的ASIC设计[J].计算机技术与发展,2008,(09):159.
　YU Zhi-qiang,RONG Meng-tian,YUAN Dan-shou.ASIC Design of an FIFO Involving Single Port SRAM[J].,2008,(04):159.
[11]唐小敏,舒远仲,刘文祥,等.基于 SSD 深度网络的河道漂浮物检测技术研究[J].计算机技术与发展,2020,30(09):154.[doi:10. 3969 / j. issn. 1673-629X. 2020. 09. 028]
　TANG Xiao-min,SHU Yuan-zhong,LIU Wen-xiang,et al.Research on River Floating Object Detection Technology Based on SSD Deep Network[J].,2020,30(04):154.[doi:10. 3969 / j. issn. 1673-629X. 2020. 09. 028]
[12]杨记鑫,胡伟霞,赵 杰,等.基于生成对抗网络的图像超分辨算法[J].计算机技术与发展,2022,32(04):57.[doi:10. 3969 / j. issn. 1673-629X. 2022. 04. 010]
　YANG Ji-xin,HU Wei-xia,ZHAO Jie,et al.Image Super Resolution Algorithm Based on Generative Countermeasure Network[J].,2022,32(04):57.[doi:10. 3969 / j. issn. 1673-629X. 2022. 04. 010]
[13]杨朝晨,陈佳悦,邢 可,等.基于改进的 DSSD 的小目标检测算法研究[J].计算机技术与发展,2022,32(06):63.[doi:10. 3969 / j. issn. 1673-629X. 2022. 06. 011]
　YANG Zhao-chen,CHEN Jia-yue,XING Ke,et al.Small Target Detection Algorithm Based on Improved DSSD[J].,2022,32(04):63.[doi:10. 3969 / j. issn. 1673-629X. 2022. 06. 011]
[14]李 郸,梁洲源,马慧宇,等.机器视觉在卷烟生产中的应用与展望[J].计算机技术与发展,2024,34(01):213.[doi:10. 3969 / j. issn. 1673-629X. 2024. 01. 031]
　LI Dan,LIANG Zhou-yuan,MA Hui-yu,et al.Application and Prospect of Machine Vision in Cigarette Production[J].,2024,34(04):213.[doi:10. 3969 / j. issn. 1673-629X. 2024. 01. 031]
[15]杨淼,夏骏,李金亮,等.用于配网线缆识别和定位的多传感器引导系统[J].计算机技术与发展,2024,34(09):202.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0175]
　YANG Miao,XIA Jun,LI Jin-liang,et al.Multi-sensor Guidance System for Identification and Localization of Distribution Network Cables[J].,2024,34(04):202.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0175]
[16]柴玉晶,梁坤豪,杨历省,等.用于点云语义分割的局部特征增强网络[J].计算机技术与发展,2025,(03):49.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0340]
　CHAI Yu-jing,LIANG Kun-hao,YANG Li-sheng,et al.Local Feature Enhancement Network for Point Cloud Semantic Segmentation[J].,2025,(04):49.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0340]

更新日期/Last Update: 2020-04-10