Khandokar, I. and Hasan, Md M. and Ernawan, F. and Islam, Md Shofiqul and Kabir, M. N. (2021) Handwritten character recognition using convolutional neural network. In: Journal of Physics: Conference Series; 7th International Conference on Mathematics, Science, and Education 2020, ICMSE 2020 , 6 October 2020 , Semarang, Virtual. pp. 1-6., 1918 (042152). ISSN 1742-6596
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Abstract
Handwritten character recognition (HCR) is the detection of characters from images, documents and other sources and changes them in machine-readable shape for further processing. The accurate recognition of intricate-shaped compound handwritten characters is still a great challenge. Recent advances in convolutional neural network (CNN) have made great progress in HCR by learning discriminatory characteristics from large amounts of raw data. In this paper, CNN is implemented to recognize the characters from a test dataset. The main focus of this work is to investigate CNN capability to recognize the characters from the image dataset and the accuracy of recognition w implementation is experimented with the dataset NIST to obtain the accuracy of handwritten characters. Test result provides that an accuracy of 92.91% accuracy is obtained on 200 images with a training set of 1000 images from NIST.
| Item Type: | Conference or Workshop Item (Lecture) |
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| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Handwritten character recognition; convolutional neural network; dataset NIST; ekk |
| Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software |
| Faculty/Division: | Institute of Postgraduate Studies Faculty of Computing |
| Depositing User: | Miss. Ratna Wilis Haryati Mustapa |
| Date Deposited: | 19 Aug 2021 01:25 |
| Last Modified: | 21 Jul 2022 05:23 |
| URI: | http://umpir.ump.edu.my/id/eprint/31546 |
| Download Statistic: | View Download Statistics |
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