1. 改进卷积神经网络的手写试卷分数识别方法.
- Author
-
仝梦园, 金守峰, 陈 阳, 李 毅, and 尹加杰
- Subjects
- *
CONVOLUTIONAL neural networks , *ARTIFICIAL neural networks , *ALGORITHMS , *HANDWRITING recognition (Computer science) , *CLASSIFICATION algorithms , *ERROR rates - Abstract
Aiming at the problems of time-consuming and high error rate in handwriting test scores statistics a handwriting test paper recognition method based on improved convolution neural network algorithm was proposed. In order to simplify the classification of the score» the score column of the handwritten test paper was divided to obtain ten types of numbers from 0 to 9. In order to improve the efficiency of handwritten score recognition, a classification and recognition algorithm of convolution neural network and Bayesian was proposed. The constructed convolution neural network model was used to extract the characteristics of handwritten digits. The PCA algorithm was used to reduce the dimensionality of the features.The hayes classifier was used to distinguish ten kinds of numbers from 0 to 9. the auuracy and efficiency of the algorithm were verified in the MNIST database.The paper score summation model was established and automatic summation is performed after recognition. The experimental results show that for the recognition uf the handwritten scores of 1 188 lest papers in 3 courses♦ the algorithm in this paper has a recognition rate of 98. 23% compared with other algorithms, the average recognition time per test paper is 7. 5 s, and verified its practicality. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF