Vol.3, No.1, 2021, pp.45-54, doi:10.32604/jcs.2021.017071
Single-Choice Aided Marking System Research Based on Back Propagation Neural Network
  • Yunzuo Zhang*, Yi Li, Wei Guo, Lei Huo, Jiayu Zhang, Kaina Guo
School of Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang, 050043, China
* Corresponding Author: Yunzuo Zhang. Email:
Received 12 January 2021; Accepted 10 March 2021; Issue published 30 April 2021
In the field of educational examination, automatic marking technology plays an essential role in improving the efficiency of marking and liberating the labor force. At present, the implementation of the policy of expanding erolments has caused a serious decline in the teacher-student ratio in colleges and universities. The traditional marking system based on Optical Mark Reader technology can no longer meet the requirements of liberating the labor force of teachers in small and medium-sized examinations. With the development of image processing and artificial neural network technology, the recognition of handwritten character in the field of pattern recognition has attracted the attention of many researchers. In this paper, filtering and de-noise processing and binary processing are used as preprocessing methods for handwriting recognition. Extract the pixel feature of handwritten characters through digital image processing of handwritten character pictures, and then, get the feature vector from these feature fragments and use it as the description of the character. The extracted feature values are used to train the neural network to realize the recognition of handwritten English letters and numerical characters. Experimental results on Chars74K and MNIST data sets show that the recognition accuracy of some handwritten English letters and numerical characters can reach 90% and 99%, respectively.
Image preprocessing; BP neural network; handwriting recognition; marking system
Cite This Article
Y. Zhang, Y. Li, W. Guo, L. Huo, J. Zhang et al., "Single-choice aided marking system research based on back propagation neural network," Journal of Cyber Security, vol. 3, no.1, pp. 45–54, 2021.
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