Handwritten character recognition using convolutional neural network
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...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IOP Publishing
2021
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/31546/ http://umpir.ump.edu.my/id/eprint/31546/1/Handwritting%20character%20recognition.pdf |
| _version_ | 1848823795653017600 |
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| author | Khandokar, I. Hasan, Md M. Ernawan, F. Islam, Md Shofiqul Kabir, M. N. |
| author_facet | Khandokar, I. Hasan, Md M. Ernawan, F. Islam, Md Shofiqul Kabir, M. N. |
| author_sort | Khandokar, I. |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T03:02:49Z |
| format | Conference or Workshop Item |
| id | ump-31546 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:02:49Z |
| publishDate | 2021 |
| publisher | IOP Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-315462022-07-21T05:23:33Z http://umpir.ump.edu.my/id/eprint/31546/ Handwritten character recognition using convolutional neural network Khandokar, I. Hasan, Md M. Ernawan, F. Islam, Md Shofiqul Kabir, M. N. QA Mathematics QA75 Electronic computers. Computer science QA76 Computer software 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. IOP Publishing 2021 Conference or Workshop Item PeerReviewed pdf en cc_by http://umpir.ump.edu.my/id/eprint/31546/1/Handwritting%20character%20recognition.pdf 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 (Published) https://iopscience.iop.org/journal/1742-6596 doi:10.1088/1742-6596/1918/4/042152 |
| spellingShingle | QA Mathematics QA75 Electronic computers. Computer science QA76 Computer software Khandokar, I. Hasan, Md M. Ernawan, F. Islam, Md Shofiqul Kabir, M. N. Handwritten character recognition using convolutional neural network |
| title | Handwritten character recognition using convolutional neural network |
| title_full | Handwritten character recognition using convolutional neural network |
| title_fullStr | Handwritten character recognition using convolutional neural network |
| title_full_unstemmed | Handwritten character recognition using convolutional neural network |
| title_short | Handwritten character recognition using convolutional neural network |
| title_sort | handwritten character recognition using convolutional neural network |
| topic | QA Mathematics QA75 Electronic computers. Computer science QA76 Computer software |
| url | http://umpir.ump.edu.my/id/eprint/31546/ http://umpir.ump.edu.my/id/eprint/31546/ http://umpir.ump.edu.my/id/eprint/31546/ http://umpir.ump.edu.my/id/eprint/31546/1/Handwritting%20character%20recognition.pdf |