Recognition of isolated handwritten Arabic characters
The challenges that face the handwritten Arabic recognition are overwhelming such as different varieties of handwriting and few public databases available. Also, teaching the non-Arabic speaker at the young age is very difficult due to the unfamiliarity of the words and meanings. So, this project...
| Main Authors: | , |
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| Format: | Proceeding Paper |
| Language: | English English English |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2019
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/79643/ http://irep.iium.edu.my/79643/1/79643_Recognition%20of%20Isolated%20Handwritten_complete.pdf http://irep.iium.edu.my/79643/7/79643_Recognition%20of%20Isolated%20Handwritten_conf..pdf http://irep.iium.edu.my/79643/8/79643_Recognition%20of%20Isolated%20Handwritten_wos.pdf |
| _version_ | 1848788807269220352 |
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| author | Almansari, Osamah Abdulrahman Nik Hashim, Nik Nur Wahidah |
| author_facet | Almansari, Osamah Abdulrahman Nik Hashim, Nik Nur Wahidah |
| author_sort | Almansari, Osamah Abdulrahman |
| building | IIUM Repository |
| collection | Online Access |
| description | The challenges that face the handwritten Arabic recognition are overwhelming such as different varieties of handwriting and
few public databases available. Also, teaching the non-Arabic speaker at the young age is very difficult due to the
unfamiliarity of the words and meanings. So, this project is focused on building a model of a deep learning architecture with
convolutional neural network (CNN) and multilayer perceptron (MLP) neural network by using python programming
language. This project analyzes the performance of a public database which is Arabic Handwritten Characters Dataset
(AHCD). However, training this database with CNN model has achieved a test accuracy of 95.27% while training it with MLP
model achieved 72.08%. Therefore, the CNN model is suitable to be used in the application device. |
| first_indexed | 2025-11-14T17:46:41Z |
| format | Proceeding Paper |
| id | iium-79643 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English English English |
| last_indexed | 2025-11-14T17:46:41Z |
| publishDate | 2019 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-796432020-07-09T04:45:05Z http://irep.iium.edu.my/79643/ Recognition of isolated handwritten Arabic characters Almansari, Osamah Abdulrahman Nik Hashim, Nik Nur Wahidah T Technology (General) TA Engineering (General). Civil engineering (General) The challenges that face the handwritten Arabic recognition are overwhelming such as different varieties of handwriting and few public databases available. Also, teaching the non-Arabic speaker at the young age is very difficult due to the unfamiliarity of the words and meanings. So, this project is focused on building a model of a deep learning architecture with convolutional neural network (CNN) and multilayer perceptron (MLP) neural network by using python programming language. This project analyzes the performance of a public database which is Arabic Handwritten Characters Dataset (AHCD). However, training this database with CNN model has achieved a test accuracy of 95.27% while training it with MLP model achieved 72.08%. Therefore, the CNN model is suitable to be used in the application device. Institute of Electrical and Electronics Engineers Inc. 2019-10 Proceeding Paper PeerReviewed application/pdf en http://irep.iium.edu.my/79643/1/79643_Recognition%20of%20Isolated%20Handwritten_complete.pdf application/pdf en http://irep.iium.edu.my/79643/7/79643_Recognition%20of%20Isolated%20Handwritten_conf..pdf application/pdf en http://irep.iium.edu.my/79643/8/79643_Recognition%20of%20Isolated%20Handwritten_wos.pdf Almansari, Osamah Abdulrahman and Nik Hashim, Nik Nur Wahidah (2019) Recognition of isolated handwritten Arabic characters. In: 7th International Conference on Mechatronics Engineering, ICOM 2019; Putrajaya; Malaysia, 30 - 31 Oct 2019, Putrajaya, Malaysia. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1300954 10.1109/ICOM47790.2019.8952035 |
| spellingShingle | T Technology (General) TA Engineering (General). Civil engineering (General) Almansari, Osamah Abdulrahman Nik Hashim, Nik Nur Wahidah Recognition of isolated handwritten Arabic characters |
| title | Recognition of isolated handwritten Arabic characters |
| title_full | Recognition of isolated handwritten Arabic characters |
| title_fullStr | Recognition of isolated handwritten Arabic characters |
| title_full_unstemmed | Recognition of isolated handwritten Arabic characters |
| title_short | Recognition of isolated handwritten Arabic characters |
| title_sort | recognition of isolated handwritten arabic characters |
| topic | T Technology (General) TA Engineering (General). Civil engineering (General) |
| url | http://irep.iium.edu.my/79643/ http://irep.iium.edu.my/79643/ http://irep.iium.edu.my/79643/ http://irep.iium.edu.my/79643/1/79643_Recognition%20of%20Isolated%20Handwritten_complete.pdf http://irep.iium.edu.my/79643/7/79643_Recognition%20of%20Isolated%20Handwritten_conf..pdf http://irep.iium.edu.my/79643/8/79643_Recognition%20of%20Isolated%20Handwritten_wos.pdf |