Recognition of Handwritten Arabic and Hindi Numerals Using Convolutional Neural Networks
Arabic and Hindi handwritten numeral detection and classification is one of the most popular fields in the automation research. It has many applications in different fields. Automatic detection and automatic classification of handwritten numerals have persistently received attention from researchers...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/4/1573 |
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| author | Amin Alqudah Ali Mohammad Alqudah Hiam Alquran Hussein R. Al-Zoubi Mohammed Al-Qodah Mahmood A. Al-Khassaweneh |
| author_facet | Amin Alqudah Ali Mohammad Alqudah Hiam Alquran Hussein R. Al-Zoubi Mohammed Al-Qodah Mahmood A. Al-Khassaweneh |
| author_sort | Amin Alqudah |
| collection | DOAJ |
| description | Arabic and Hindi handwritten numeral detection and classification is one of the most popular fields in the automation research. It has many applications in different fields. Automatic detection and automatic classification of handwritten numerals have persistently received attention from researchers around the world due to the robotic revolution in the past decades. Therefore, many great efforts and contributions have been made to provide highly accurate detection and classification methodologies with high performance. In this paper, we propose a two-stage methodology for the detection and classification of Arabic and Hindi handwritten numerals. The classification was based on convolutional neural networks (CNNs). The first stage of the methodology is the detection of the input numeral to be either Arabic or Hindi. The second stage is to detect the input numeral according to the language it came from. The simulation results show very high performance; the recognition rate was close to 100%. |
| first_indexed | 2024-03-09T04:56:54Z |
| format | Article |
| id | doaj.art-1bf09834893c4b48b218e12d25133ef2 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T04:56:54Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-1bf09834893c4b48b218e12d25133ef22023-12-03T13:03:51ZengMDPI AGApplied Sciences2076-34172021-02-01114157310.3390/app11041573Recognition of Handwritten Arabic and Hindi Numerals Using Convolutional Neural NetworksAmin Alqudah0Ali Mohammad Alqudah1Hiam Alquran2Hussein R. Al-Zoubi3Mohammed Al-Qodah4Mahmood A. Al-Khassaweneh5Department of Computer Engineering, Yarmouk University, Irbid 21163, JordanDepartment of Biomedical Systems and Informatics Engineering, Yarmouk University, Irbid 21163, JordanDepartment of Biomedical Systems and Informatics Engineering, Yarmouk University, Irbid 21163, JordanDepartment of Computer Engineering, Yarmouk University, Irbid 21163, JordanDepartment of Electrical Engineering, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi ArabiaDepartment of Computer Engineering, Yarmouk University, Irbid 21163, JordanArabic and Hindi handwritten numeral detection and classification is one of the most popular fields in the automation research. It has many applications in different fields. Automatic detection and automatic classification of handwritten numerals have persistently received attention from researchers around the world due to the robotic revolution in the past decades. Therefore, many great efforts and contributions have been made to provide highly accurate detection and classification methodologies with high performance. In this paper, we propose a two-stage methodology for the detection and classification of Arabic and Hindi handwritten numerals. The classification was based on convolutional neural networks (CNNs). The first stage of the methodology is the detection of the input numeral to be either Arabic or Hindi. The second stage is to detect the input numeral according to the language it came from. The simulation results show very high performance; the recognition rate was close to 100%.https://www.mdpi.com/2076-3417/11/4/1573Arabic numeralsCNNdeep learninghandwrittennumerals Hindi numerals |
| spellingShingle | Amin Alqudah Ali Mohammad Alqudah Hiam Alquran Hussein R. Al-Zoubi Mohammed Al-Qodah Mahmood A. Al-Khassaweneh Recognition of Handwritten Arabic and Hindi Numerals Using Convolutional Neural Networks Applied Sciences Arabic numerals CNN deep learning handwritten numerals Hindi numerals |
| title | Recognition of Handwritten Arabic and Hindi Numerals Using Convolutional Neural Networks |
| title_full | Recognition of Handwritten Arabic and Hindi Numerals Using Convolutional Neural Networks |
| title_fullStr | Recognition of Handwritten Arabic and Hindi Numerals Using Convolutional Neural Networks |
| title_full_unstemmed | Recognition of Handwritten Arabic and Hindi Numerals Using Convolutional Neural Networks |
| title_short | Recognition of Handwritten Arabic and Hindi Numerals Using Convolutional Neural Networks |
| title_sort | recognition of handwritten arabic and hindi numerals using convolutional neural networks |
| topic | Arabic numerals CNN deep learning handwritten numerals Hindi numerals |
| url | https://www.mdpi.com/2076-3417/11/4/1573 |
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