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| Abstract : Universiti Pendidikan Sultan Idris |
| 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. ? 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of KES International. |
| References |
Ahmed, E., Jones, M., & Marks, T. K. (2015). An improved deep learning architecture for person re-identification. Paper presented at the Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, , 07-12-June-2015 3908-3916. doi:10.1109/CVPR.2015.7299016 Retrieved from www.scopus.com Ahmed, E., Jones, M., & Marks, T. K. (2015). An improved deep learning architecture for person re-identification. Paper presented at the Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, , 07-12-June-2015 3908-3916. doi:10.1109/CVPR.2015.7299016 Retrieved from www.scopus.com Albelwi, S., & Mahmood, A. (2017). A framework for designing the architectures of deep convolutional neural networks. Entropy, 19(6) doi:10.3390/e19060242 Albelwi, S., & Mahmood, A. (2016). Analysis of instance selection algorithms on large datasets with deep convolutional neural networks. Paper presented at the 2016 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2016, doi:10.1109/LISAT.2016.7494142 Retrieved from www.scopus.com Andonie, R. (2019). Hyperparameter optimization in learning systems. Journal of Membrane Computing, 1(4), 279-291. doi:10.1007/s41965-019-00023-0 Brem, A., Viardot, E., & Nylund, P. A. (2021). Implications of the coronavirus (COVID-19) outbreak for innovation: Which technologies will improve our lives? Technological Forecasting and Social Change, 163 doi:10.1016/j.techfore.2020.120451 Das, N., Sarkar, R., Basu, S., Kundu, M., Nasipuri, M., & Basu, D. K. (2012). A genetic algorithm based region sampling for selection of local features in handwritten digit recognition application. Applied Soft Computing Journal, 12(5), 1592-1606. doi:10.1016/j.asoc.2011.11.030 Dozat, T. (2015). Incorporating nesterov momentum into adam. Incorporating Nesterov Momentum into Adam, Retrieved from www.scopus.com Feng, W. E. I., & Shan, L. (2020). A study on handwritten digital recognition technology based on CNN optimization. Journal of Lianyungang Technical College, Retrieved from www.scopus.com Gonzalez, C. I., Melin, P., Castro, J. R., Mendoza, O., & Castillo, O. (2016). An improved sobel edge detection method based on generalized type-2 fuzzy logic. Soft Computing, 20(2), 773-784. doi:10.1007/s00500-014-1541-0 Hosseini-Asl, E., & Guha, A. (2015). Similarity-based text recognition by deeply supervised siamese network. Proceedings of Future Technologies Conference, , 1-7. Retrieved from www.scopus.com Hosseini-Asl, E., & Guha, A. (2015). Similarity-based text recognition by deeply supervised siamese network. Proceedings of Future Technologies Conference, , 1-7. Retrieved from www.scopus.com LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2323. doi:10.1109/5.726791 Li, C., & Lalani, F. (2020). The COVID-19 pandemic has changed education forever. The COVID-19 Pandemic has Changed Education Forever.this is how, Retrieved from www.scopus.com Liu, X., Cao, Y., & Lu, P. (2014). Research on optical image encryption technique with compressed sensing. Guangxue Xuebao/Acta Optica Sinica, 34(3) doi:10.3788/AOS201434.0307002 Liu, X., Cao, Y., Lu, P., Lu, X., & Li, Y. (2013). Optical image encryption technique based on compressed sensing and arnold transformation. Optik, 124(24), 6590-6593. doi:10.1016/j.ijleo.2013.05.092 Lv, G. (2011). Recognition of multi-fontstyle characters based on convolutional neural network. Paper presented at the Proceedings - 2011 4th International Symposium on Computational Intelligence and Design, ISCID 2011, , 2 223-225. doi:10.1109/ISCID.2011.157 Retrieved from www.scopus.com Lv, G. (2011). Recognition of multi-fontstyle characters based on convolutional neural network. Paper presented at the Proceedings - 2011 4th International Symposium on Computational Intelligence and Design, ISCID 2011, , 2 223-225. doi:10.1109/ISCID.2011.157 Retrieved from www.scopus.com Reddi, S. J., Kale, S., & Kumar, S. (2018). On the convergence of adam and beyond. International Conference on Learning Representations, , 1-23. Retrieved from www.scopus.com Ren, M. -., & Meng, L. (2015). Handwriting digit recognition based on prototype generation technique. Computer Engineering and Design, (8), 2211-2216. Retrieved from www.scopus.com Shopon, M., Mohammed, N., & Abedin, M. A. (2017). Image augmentation by blocky artifact in deep convolutional neural network for handwritten digit recognition. Paper presented at the 2017 IEEE International Conference on Imaging, Vision and Pattern Recognition, icIVPR 2017, doi:10.1109/ICIVPR.2017.7890867 Retrieved from www.scopus.com Sun, Y., Wang, X., & Tang, X. (2013). Deep convolutional network cascade for facial point detection. Paper presented at the Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 3476-3483. doi:10.1109/CVPR.2013.446 Retrieved from www.scopus.com Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., . . . Rabinovich, A. (2015). Going deeper with convolutions. Paper presented at the Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, , 07-12-June-2015 1-9. doi:10.1109/CVPR.2015.7298594 Retrieved from www.scopus.com Talathi, S. S. (2015). Hyper-parameter optimization of deep convolutional networks for object recognition. Paper presented at the Proceedings - International Conference on Image Processing, ICIP, , 2015-December 3982-3986. doi:10.1109/ICIP.2015.7351553 Retrieved from www.scopus.com Wang, Y., Quan, C., & Tay, C. J. (2016). Asymmetric optical image encryption based on an improved amplitude-phase retrieval algorithm. Optics and Lasers in Engineering, 78, 8-16. doi:10.1016/j.optlaseng.2015.09.008 Zeiler, M. D., & Fergus, R. (2013). Stochastic pooling for regularization of deep convolutional neural networks. Paper presented at the 1st International Conference on Learning Representations, ICLR 2013 - Conference Track Proceedings, Retrieved from www.scopus.com ZHAO, Y., & WU, H. (2013). Handwritten numeral recognition based on multi-scale features and neural network. Computer Science, 40(8), 316-318. Retrieved from www.scopus.com |
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