Advancements in CNN Architectures for Offline Handwritten Arabic Character Recognition
Analyzing and classifying images of Arabic handwritten characters is crucial for text understanding and interpretation from image data. The recognition of handwritten Arabic characters not only preserves the integrity of the Arabic language but also enhances computer vision applications tailored for...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | E3S Web of Conferences |
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| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/01/e3sconf_icegc2024_00015.pdf |
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| author | El Ibrahimi Aissam Elzaar Abdellah El Akchioui Nabil Benaya Nabil El Allati Abderrahim |
| author_facet | El Ibrahimi Aissam Elzaar Abdellah El Akchioui Nabil Benaya Nabil El Allati Abderrahim |
| author_sort | El Ibrahimi Aissam |
| collection | DOAJ |
| description | Analyzing and classifying images of Arabic handwritten characters is crucial for text understanding and interpretation from image data. The recognition of handwritten Arabic characters not only preserves the integrity of the Arabic language but also enhances computer vision applications tailored for Arabic script. Existing literature often proposes complex architectures, which can hinder real-time prediction speed and accuracy. In this paper, we propose a novel Deep Learning architecture based on Convolutional Neural Networks (CNNs) for accurate classification of Arabic handwritten characters. Our approach offers simplicity without compromising accuracy, making it suitable for online recognition tasks. We validate our method on the Arabic Handwritten Characters Database (AHCD) and achieve a high recognition rate of 99%. The trained model demonstrates robust performance, indicating its potential for practical applications in Arabic character recognition. |
| format | Article |
| id | doaj-art-053f456c849844f79b0d0bd3f9fd3824 |
| institution | Kabale University |
| issn | 2267-1242 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj-art-053f456c849844f79b0d0bd3f9fd38242025-02-05T10:46:25ZengEDP SciencesE3S Web of Conferences2267-12422025-01-016010001510.1051/e3sconf/202560100015e3sconf_icegc2024_00015Advancements in CNN Architectures for Offline Handwritten Arabic Character RecognitionEl Ibrahimi Aissam0Elzaar Abdellah1El Akchioui Nabil2Benaya Nabil3El Allati Abderrahim4Laboratory of R&D in Engineering Sciences, FST Al-Hoceima, Abdelmalek Essaadi UniversityLaboratory of R&D in Engineering Sciences, FST Al-Hoceima, Abdelmalek Essaadi UniversityLaboratory of R&D in Engineering Sciences, FST Al-Hoceima, Abdelmalek Essaadi UniversityLaboratory of R&D in Engineering Sciences, FST Al-Hoceima, Abdelmalek Essaadi UniversityLaboratory of R&D in Engineering Sciences, FST Al-Hoceima, Abdelmalek Essaadi UniversityAnalyzing and classifying images of Arabic handwritten characters is crucial for text understanding and interpretation from image data. The recognition of handwritten Arabic characters not only preserves the integrity of the Arabic language but also enhances computer vision applications tailored for Arabic script. Existing literature often proposes complex architectures, which can hinder real-time prediction speed and accuracy. In this paper, we propose a novel Deep Learning architecture based on Convolutional Neural Networks (CNNs) for accurate classification of Arabic handwritten characters. Our approach offers simplicity without compromising accuracy, making it suitable for online recognition tasks. We validate our method on the Arabic Handwritten Characters Database (AHCD) and achieve a high recognition rate of 99%. The trained model demonstrates robust performance, indicating its potential for practical applications in Arabic character recognition.https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/01/e3sconf_icegc2024_00015.pdfandwritten arabic character recognitiondeep learningobject recognitionconvolutional neural networkoffline arabic handwritten recognition (oahr) |
| spellingShingle | El Ibrahimi Aissam Elzaar Abdellah El Akchioui Nabil Benaya Nabil El Allati Abderrahim Advancements in CNN Architectures for Offline Handwritten Arabic Character Recognition E3S Web of Conferences andwritten arabic character recognition deep learning object recognition convolutional neural network offline arabic handwritten recognition (oahr) |
| title | Advancements in CNN Architectures for Offline Handwritten Arabic Character Recognition |
| title_full | Advancements in CNN Architectures for Offline Handwritten Arabic Character Recognition |
| title_fullStr | Advancements in CNN Architectures for Offline Handwritten Arabic Character Recognition |
| title_full_unstemmed | Advancements in CNN Architectures for Offline Handwritten Arabic Character Recognition |
| title_short | Advancements in CNN Architectures for Offline Handwritten Arabic Character Recognition |
| title_sort | advancements in cnn architectures for offline handwritten arabic character recognition |
| topic | andwritten arabic character recognition deep learning object recognition convolutional neural network offline arabic handwritten recognition (oahr) |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/01/e3sconf_icegc2024_00015.pdf |
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