Name
Abstract | ||
|---|---|---|
The pandemic of Covid-19 has caused a shift of paradigm of education, from face-to-face to e-learning. E-learning leads to an escalation in digitalization of handwritten documents because it requires submission of homework and assignments through online. To help teachers in checking digitalized handwritten homework, this paper proposes an automatic checking system based on a convolutional neural network (CNN) for handwritten numeral recognition. The CNN is used to recognize four arithmetic operations in mathematical questions consisting of addition, deduction, multiplication and division. The performance CNN in handwritten numeral recognition have been optimized in terms of activation function and gradient descent algorithm. The proposed CNN is also trained and tested with the MNIST handwritten data set. The experimental results show that the recognition accuracy the improved CNN improves to a certain extent as compared to before optimization. (C) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://crativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of KES International. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.procs.2021.09.218 | KNOWLEDGE-BASED AND INTELLIGENT INFORMATION & ENGINEERING SYSTEMS (KSE 2021) |
Keywords | DocType | Volume |
deep learning, CNN, handwritten numeral recognition, image processing | Conference | 192 |
ISSN | Citations | PageRank |
1877-0509 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chen ShanWei | 1 | 0 | 0.34 |
| Shir LiWang | 2 | 0 | 0.34 |
| Theam Foo Ng | 3 | 41 | 4.86 |
| Dzati Athiar Ramli | 4 | 33 | 8.97 |