Open Access
ARTICLE
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:
Journal of Cyber Security 2021, 3(1), 45-54. https://doi.org/10.32604/jcs.2021.017071
Received 12 January 2021; Accepted 10 March 2021; Issue published 30 April 2021
Abstract
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.
Keywords
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.