New Rules to Enhance the Performances of Histogram Projection for Segmenting Small-Sized Arabic Words

Amara, Marwa; Zidi, Kamel; Ghédira, Khaled; Zidi, Salah

doi:10.1007/978-3-319-27221-4_14

“…This segmentation approach has shown very high performance on a single font but failed to maintain this high performance when tested on multiple fonts and font sizes. Lots of efforts [8]- [10] in Arabic character segmentation have been based on histogram analysis of words.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Language-Independent Multi-Font OCR for Arabic Script

Osman¹,

Zaghw²,

Hazem³

et al. 2020

Preprint

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Optical Character Recognition (OCR) is the process of extracting digitized text from images of scanned documents. While OCR systems have already matured in many languages, they still have shortcomings in cursive languages with overlapping letters such as the Arabic language. This paper proposes a complete Arabic OCR system that takes a scanned image of Arabic Naskh script as an input and generates a corresponding digital document. Our Arabic OCR system consists of the following modules: Pre-processing, Word-level Feature Extraction, Character Segmentation, Character Recognition, and Post-processing. This paper also proposes an improved font-independent character segmentation algorithm that outperforms the state-of-the-art segmentation algorithms. Lastly, the paper proposes a neural network model for the character recognition task. The system has experimented on several open Arabic corpora datasets with an average character segmentation accuracy 98.06%, character recognition accuracy 99.89%, and overall system accuracy 97.94% achieving outstanding results compared to the state-ofthe-art Arabic OCR systems.

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“…This segmentation approach has shown very high performance on a single font but failed to maintain this high performance when tested on multiple fonts and font sizes. Lots of efforts [8]- [10] in Arabic character segmentation have been based on histogram analysis of words. It is important to mention that Qaroush et al [8] also proposes a very effective word segmentation methodology that achieves state-of-the-art accuracy by implementing cut identification and filtration through gap length.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Language-Independent Multi-Font OCR for Arabic Script

Osman¹,

Zaghw²,

Hazem³

et al. 2020

Computer Science &Amp; Information Technology (CS &Amp; IT)

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Optical Character Recognition (OCR) is the process of extracting digitized text from images of scanned documents. While OCR systems have already matured in many languages, they still have shortcomings in cursive languages with overlapping letters such as the Arabic language. This paper proposes a complete Arabic OCR system that takes a scanned image of Arabic Naskh script as an input and generates a corresponding digital document. Our Arabic OCR system consists of the following modules: Pre-processing, Word-level Feature Extraction, Character Segmentation, Character Recognition, and Post-processing. This paper also proposes an improved font-independent character segmentation algorithm that outperforms the state-of-the-art segmentation algorithms. Lastly, the paper proposes a neural network model for the character recognition task. The system has experimented on several open Arabic corpora datasets with an average character segmentation accuracy 98.06%, character recognition accuracy 99.89%, and overall system accuracy 97.94% achieving outstanding results compared to the state-of-the-art Arabic OCR systems.

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“…Segmentation of Arabic text thus can be more difficult and time consuming for the development of Arabic OCR systems [3]. Correspondingly, segmentation has been considered as the main contribution for increasing the recognition error rate in Arabic OCR systems [46], [62], [63].…”

Section: B Segmentation Phasementioning

confidence: 99%

“…However, this method has been tested on specific Arabic fonts which do not contain overlapping and ligatures. Consequently, this method would not be appropriate for Arabic fonts that have ligatures, such as traditional Arabic font [3], [62].…”

Section: ) Explicit Segmentationmentioning

confidence: 99%

See 1 more Smart Citation

Printed Arabic Script Recognition: A Survey

Alghamdi¹,

Teahan²

2018

ijacsa

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Optical character recognition (OCR) is essential in various real-world applications, such as digitizing learning resources to assist visually impaired people and transforming printed resources into electronic media. However, the development of OCR for printed Arabic script is a challenging task. These challenges are due to the specific characteristics of Arabic script. Therefore, different methods have been proposed for developing Arabic OCR systems, and this paper aims to provide a comprehensive review of these methods. This paper also discusses relevant issues of printed Arabic OCR including the challenges of printed Arabic script and performance evaluation. It concludes with a discussion of the current status of printed Arabic OCR, analyzing the remaining problems in the field of printed Arabic OCR and providing several directions for future research.

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New Rules to Enhance the Performances of Histogram Projection for Segmenting Small-Sized Arabic Words

Cited by 12 publications

References 7 publications

An Efficient Language-Independent Multi-Font OCR for Arabic Script

An Efficient Language-Independent Multi-Font OCR for Arabic Script

An Efficient Language-Independent Multi-Font OCR for Arabic Script

Printed Arabic Script Recognition: A Survey

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