An evolutionary harmony search algorithm with dominant point detection for recognition-based segmentation of online Arabic text recognition

Potrus, Moayad Yousif; Ngah, Umi Kalthum; Ahmed, Bestoun S.

doi:10.1016/j.asej.2014.05.003

Cited by 15 publications

(8 citation statements)

References 15 publications

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“…One of the main useful features of the proposed scheme is automatic and enhanced word identification. Beside the Chinese language, there are studies which applied word segmentation for Urdu [26], Russian [40], Arabic [13], Vietnamese [52], Akkadian [55], Indian [59] and South Indian [59]. However, other studies [22] [44] and [32] applied sentence segmentation to analyze Arabic, Greek, and Malay languages accordingly.…”

Section: Resultsmentioning

confidence: 99%

“…Another reason is each segment is used as units of analysis and access [3]. Text segmentation was used to process text in emotion extraction [6], sentiment mining [7] [8], opinion mining [9] [10], topic identification [11] [12], language detection [13] and information retrieval [14]. Sentiment analyzing within the text covers wide range of techniques, but most of them include segmentation stage in text process.…”

Section: Introductionmentioning

confidence: 99%

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Text Segmentation Techniques: A Critical Review

Pak

Teh

2017

Studies in Computational Intelligence

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Text segmentation is widely used for processing text. It is a method of splitting a document into smaller parts, which is usually called segments. Each segment has its relevant meaning. Those segments categorized as word, sentence, topic, phrase or any information unit depending on the task of the text analysis. This study presents various reasons of usage of text segmentation for different analyzing approaches. We categorized the types of documents and languages used. The main contribution of this study includes a summarization of 50 research papers and an illustration of past decade (January 2007-January 2017)'s of research that applied text segmentation as their main approach for analysing text. Results revealed the popularity of using text segmentation in different languages. Besides that, the "word" seems to be the most practical and usable segment, as it is the smaller unit than the phrase, sentence or line.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Text Segmentation Techniques: A Critical Review

Pak

Teh

2017

Studies in Computational Intelligence

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“…Potrus et al [52] proposed a hybrid handwritten Arabic text recognition technique. They leveraged a manually collected dataset consisting of 4500 Arabic words to conduct the study.…”

Section: ) Fusion-based Image Retrievalmentioning

confidence: 99%

Manuscripts Image Retrieval Using Deep Learning Incorporating a Variety of Fusion Levels

Khayyat

Elrefaei

2020

IEEE Access

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The instantaneous search and retrieval of the most relevant images to a specific query image is a desirable application for all digital libraries. The automatic extraction and classification according to the most distinguishable features, is a crucial step to detect the similarities among images successfully. This study introduces a novel approach that utilizes a fusion model for classifying and retrieving historical Arabic manuscripts' images. To accomplish our goal, the images are first classified according to their extracted deep learning visual features utilizing a pre-trained convolutional neural network. Then, the texts written in the manuscripts' images are extracted and pre-processed to classify the images according to their textual features using an optimized bidirectional LSTM deep learning model with attention and batch normalization layers. Finally, both the visual and textual deep learning models are fused at three different fusion-levels named: decision-level, features-level, and score-level. The score-level fusion model resulted in a considerable improvement of each model used individually. Extensive experimentation and evaluation of the proposed fusion method on the collected ancient Arabic manuscripts dataset proved its robustness against other stateof-the-art methods recording 99% classification accuracy and 98% mean accuracy on the top-10 image retrieval.

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“…Figure 1 illustrates the numbers of percentage for each language beside English used in evaluated studies. For instance, studies of [5] [17], Arabic [11], Vietnamese [33], and Akkadian [35]. However, other studies [13] [29][22] applied sentence segmentation to analyse Japanese, Greek and Malay languages accordingly.…”

Section: Review Methodsmentioning

confidence: 99%

“…Another reason is segments can be used as units of analysis and access [2]. Text segmentation was used to process text in opinion mining [5] [6], information retrieval [7], emotion extraction [8], sentiment mining [9] [10] and language detection [11]. This paper reviews different methods and reasons of applying text segmentation in opinion and sentiment mining, language detection and information retrieval.…”

Section: Introductionmentioning

confidence: 99%

Text Segmentation for Analysing Different Languages

Pak¹,

Teh²

2017

Proceedings of the First EAI International Conference on Computer Science and Engineering

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Abstract. Over the past several years, researchers have applied different methods of text segmentation. Text segmentation is defined as a method of splitting a document into smaller segments, assuming with its own relevant meaning. Those segments can be classified into the tag, word, sentence, topic, phrase and any information unit. Firstly, this study reviews the different types of text segmentation methods used in different types of documentation, and later discusses the various reasons for utilizing it in opinion mining. The main contribution of this study includes a summarisation of research papers from the past 10 years that applied text segmentation as their main approach in text analysing. Results show that word segmentation was successfully and widely used for processing different languages.

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An evolutionary harmony search algorithm with dominant point detection for recognition-based segmentation of online Arabic text recognition

Cited by 15 publications

References 15 publications

Text Segmentation Techniques: A Critical Review

Text Segmentation Techniques: A Critical Review

Manuscripts Image Retrieval Using Deep Learning Incorporating a Variety of Fusion Levels

Text Segmentation for Analysing Different Languages

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