An efficient, font independent word and character segmentation algorithm for printed Arabic text

Qaroush, Aziz; Jaber, Bassam; Mohammad, Khader; Washaha, Mahdi; Maali, Eman; Nayef, Nibal

doi:10.1016/j.jksuci.2019.08.013

Cited by 15 publications

(37 citation statements)

References 13 publications

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“…Further, also more innovative techniques such as developing more secure algorithms to government's confidential data and user data for smart governance can be developed. Our proposed framework is the basic stack of Hadoop components which may be modified according to the real-time requirement for the more effective and enhanced model for future e-governance using Big Data [22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49].…”

Section: Discussionmentioning

confidence: 99%

Benefits of using Big Data Sentiment Analysis and Soft Computing Techniques in E-Governance

Dwivedi¹,

Pant²,

Pandey³

et al. 2019

IJRTE

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In the current digital world, we tend to use electronic media to complete, each process and every work on time and quickly. In governance, we are benefited in a significant way from the use of sentiment posted electronically, called big data sentiment for e-governance. In this paper, we discuss the benefits of big data sentiment analysis and soft computing techniques for e-government with advancements of using big data. And its featured faster sentiment analysis using soft computing techniques and its framework. It helps to improve the goals of e-Governance that are Transparent, Trustworthy and Corruption free and quick action Governance as well as more citizen involvement in the country's development.

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Section: Discussionmentioning

confidence: 99%

Benefits of using Big Data Sentiment Analysis and Soft Computing Techniques in E-Governance

Dwivedi¹,

Pant²,

Pandey³

et al. 2019

IJRTE

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“…Step 5 Calculate the interval hesitation order weighted distance measure d (ci, c0). Under each factor, the standard over-segmentation evaluation value is [1,1], and the distance measure of ci and c0 is calculated by formula (1).…”

Section: B Merging Model Based On Interval-valued Hesitant Fuzzy Setmentioning

confidence: 99%

“…In the research of text image segmentation, Qaroush et al [1] presented an indirect segmentation-based algorithm in investigating text image segmentation. The proposed algorithm employed a projection profile method with the Interquartile Range statistical method to distinguish character parts in character part segmentation.…”

Section: Introductionmentioning

confidence: 99%

Ancient Chinese Character Image Segmentation Based on Interval-Valued Hesitant Fuzzy Set

Tian

Sun

2020

IEEE Access

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To address the low segmentation accuracy caused by the rich glyph styles of ancient Chinese characters and the complex layout of ancient Chinese books, which affects the retrieval and recognition results, an algorithm for the layout image analysis of ancient Chinese books and Chinese character image segmentation is proposed. The initial segmentation results were obtained through the projection method of the layout of ancient Chinese books, and the connected component analysis of the above results was carried out to determine the rough divided blocks of under-segmentation and oversegmentation. Considering under-segmentation of adhesive Chinese characters, the improved K-means clustering method was used to segment adhesive blocks to obtain single-character images. To address the over-segmentation of character components separation, a method based on interval-valued hesitant fuzzy set is proposed. This method analyzed the features of the connected component in the block, characterized the over-segmentation connected component. The hesitant fuzzy distances between other connected components and the standard merge evaluation interval number were calculated in sequence. The connected component with the smallest distance was preferentially merged with the over-segmentation connected component until no over-segmentation connected component remained in the block. The experimental segmentation accuracy was 89.94%.

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“…Inspired by NLP applications, Alkhateeb et al [6] and Radwan et al [8] proposed the use of a sliding window approach for segmentation. A Convolutional Neural Network (CNN) is used to determine the likelihood of a given sliding window consisting of several segments to be an actual character.…”

Section: Related Workmentioning

confidence: 99%

“…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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An efficient, font independent word and character segmentation algorithm for printed Arabic text

Cited by 15 publications

References 13 publications

Benefits of using Big Data Sentiment Analysis and Soft Computing Techniques in E-Governance

Benefits of using Big Data Sentiment Analysis and Soft Computing Techniques in E-Governance

Ancient Chinese Character Image Segmentation Based on Interval-Valued Hesitant Fuzzy Set

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

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