A review of Arabic text recognition dataset
Building a robust Optical Character Recognition (OCR) system for languages, such as Arabic with cursive scripts, has always been challenging. These challenges increase if the text contains diacritics of different sizes for characters and words. Apart from the complexity of the used font, these cha...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2020
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| Online Access: | http://journalarticle.ukm.my/15419/ http://journalarticle.ukm.my/15419/1/06.pdf |
| _version_ | 1848813796112465920 |
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| author | Idris Saleh Al-Sheikh, Masnizah Mohd, Lia Warlina, |
| author_facet | Idris Saleh Al-Sheikh, Masnizah Mohd, Lia Warlina, |
| author_sort | Idris Saleh Al-Sheikh, |
| building | UKM Institutional Repository |
| collection | Online Access |
| description | Building a robust Optical Character Recognition (OCR) system for languages, such as Arabic with cursive scripts,
has always been challenging. These challenges increase if the text contains diacritics of different sizes for
characters and words. Apart from the complexity of the used font, these challenges must be addressed in
recognizing the text of the Holy Quran. To solve these challenges, the OCR system would have to undergo
different phases. Each problem would have to be addressed using different approaches, thus, researchers are
studying these challenges and proposing various solutions. This has motivate this study to review Arabic OCR
dataset because the dataset plays a major role in determining the nature of the OCR systems. State-of-the-art
approaches in segmentation and recognition are discovered with the implementation of Recurrent Neural
Networks (Long Short-Term Memory-LSTM and Gated Recurrent Unit-GRU) with the use of the Connectionist
Temporal Classification (CTC). This also includes deep learning model and implementation of GRU in the Arabic
domain. This paper has contribute in profiling the Arabic text recognition dataset thus determining the nature of
OCR system developed and has identified research direction in building Arabic text recognition dataset. |
| first_indexed | 2025-11-15T00:23:53Z |
| format | Article |
| id | oai:generic.eprints.org:15419 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T00:23:53Z |
| publishDate | 2020 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org:154192020-10-23T02:59:26Z http://journalarticle.ukm.my/15419/ A review of Arabic text recognition dataset Idris Saleh Al-Sheikh, Masnizah Mohd, Lia Warlina, Building a robust Optical Character Recognition (OCR) system for languages, such as Arabic with cursive scripts, has always been challenging. These challenges increase if the text contains diacritics of different sizes for characters and words. Apart from the complexity of the used font, these challenges must be addressed in recognizing the text of the Holy Quran. To solve these challenges, the OCR system would have to undergo different phases. Each problem would have to be addressed using different approaches, thus, researchers are studying these challenges and proposing various solutions. This has motivate this study to review Arabic OCR dataset because the dataset plays a major role in determining the nature of the OCR systems. State-of-the-art approaches in segmentation and recognition are discovered with the implementation of Recurrent Neural Networks (Long Short-Term Memory-LSTM and Gated Recurrent Unit-GRU) with the use of the Connectionist Temporal Classification (CTC). This also includes deep learning model and implementation of GRU in the Arabic domain. This paper has contribute in profiling the Arabic text recognition dataset thus determining the nature of OCR system developed and has identified research direction in building Arabic text recognition dataset. Penerbit Universiti Kebangsaan Malaysia 2020-06 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/15419/1/06.pdf Idris Saleh Al-Sheikh, and Masnizah Mohd, and Lia Warlina, (2020) A review of Arabic text recognition dataset. Asia-Pacific Journal of Information Technology and Multimedia, 9 (1). pp. 69-81. ISSN 2289-2192 http://www.ukm.my/apjitm/articles-year.php |
| spellingShingle | Idris Saleh Al-Sheikh, Masnizah Mohd, Lia Warlina, A review of Arabic text recognition dataset |
| title | A review of Arabic text recognition dataset |
| title_full | A review of Arabic text recognition dataset |
| title_fullStr | A review of Arabic text recognition dataset |
| title_full_unstemmed | A review of Arabic text recognition dataset |
| title_short | A review of Arabic text recognition dataset |
| title_sort | review of arabic text recognition dataset |
| url | http://journalarticle.ukm.my/15419/ http://journalarticle.ukm.my/15419/ http://journalarticle.ukm.my/15419/1/06.pdf |