Arabic Artistic Script Style Identification Using Texture Descriptors.

Kaoudja, Zineb; Khaldi, Belal; Kherfi, Mohammed Lamine

doi:10.1109/ccssp49278.2020.9151569

Cited by 3 publications

(3 citation statements)

References 22 publications

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“…It www.ijacsa.thesai.org offers an integrated solution for the challenges of preprocessing Arabic text on social media. This was undertaken to investigate the performance metrics as given in [23,24,25,26,27,28,29] and validates the proposed model for small-and large-scale datasets. Disambiguation using the deep learning techniques with the Arabic corpus is presented in [30].…”

Section: Related Studiesmentioning

confidence: 99%

Utilizing Deep Learning in Arabic Text Classification Sentiment Analysis of Twitter

Ibrahim¹,

Yafooz²,

Emara³

et al. 2022

IJACSA

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The number of social media users has increased. These users share and reshare their ideas in posts and this information can be mined and used by decision-makers in different domains, who analyse and study user opinions on social media networks to improve the quality of products or study specific phenomena. During the COVID-19 pandemic, social media was used to make decisions to limit the spread of the disease using sentiment analysis. Substantial research on this topic has been done; however, there are limited Arabic textual resources on social media. This has resulted in fewer quality sentiment analyses on Arabic texts. This study proposes a model for Arabic sentiment analysis using a Twitter dataset and deep learning models with Arabic word embedding. It uses the supervised deep learning algorithms on the proposed dataset. The dataset contains 51,000 tweets, of which 8,820 are classified as positive, 37,360 neutral, and 8,820 as negative. After cleaning it will contain 31,413. The experiment has been carried out by applying the deep learning models, Convolutional Neural Network and Long Short-Term Memory while comparing the results of different machine learning techniques such as Naive Bayes and Support Vector Machine. The accuracy of the AraBERT model is 0.92% when applying the test on 3,505 tweets.

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Section: Related Studiesmentioning

confidence: 99%

Utilizing Deep Learning in Arabic Text Classification Sentiment Analysis of Twitter

Ibrahim¹,

Yafooz²,

Emara³

et al. 2022

IJACSA

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“…The dataset was collected from a wide range of sources and they concluded from their article that combining such data with machine learning models is adequate for enabling machine learning to read and understand Arabic calligraphy. In the work of [14] set of texture features were used to analyze Arabic artistic style. The finding is that the best performance has been yielded by the BSIF descriptor with the SVM classifier [15] suppose a new approach for developing a method for generating Arabic handwriting by testing 7 types of Arabic calligraphy in the work of [16] the author proposed a new framework of optical font recognition for Arabic calligraphy by enhancing the binarization method the [17] present computational abstractions for generating and manipulating calligraphic compositions systematic by s within an interacting environment [18] suppose multi-classifier decisions as based off or Arabic-calligraphy style classification.…”

Section: Literature Surveymentioning

confidence: 99%

Fast learning neural network based on texture for Arabic calligraphy identification

Hussein

2021

IJEECS

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<span id="docs-internal-guid-5c723154-7fff-a7b2-3582-b7c2920a9921"><span>Arabic calligraphy is considered a sort of Arabic writing art where letters in Arabic can be written in various curvy or segments styles. The efforts of automating the identification of Arabic calligraphy by using artificial intelligence were less comparing with other languages. Hence, this article proposes using four types of features and a single hidden layer neural network for training on Arabic calligraphy and predicting the type of calligraphy that is used. For neural networks, we compared the case of non-connected input and output layers in extreme learning machine ELM and the case of connected input-output layers in FLN. The prediction accuracy of fast learning machine FLN was superior comparing ELM that showed a variation in the obtained accuracy. </span></span>

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“…The extraction features commonly used in devices for the identification of unique features are defined as follows [9]. Low level: image segmentation, tracking corner, detection of blob, feature extraction, scale-invariant transformation feature; curvature: active contours, parameterized shapes; image motion: image text extraction programs and tools, such as MathWorks, MATLAB, Scilab, and NumPy [10].…”

Section: Related Workmentioning

confidence: 99%

Hybrid Trainable System for Writer Identification of Arabic Handwriting

Saleem¹,

Abdulazeez²

2021

Computers, Materials &Amp; Continua

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Writer identification (WI) based on handwritten text structures is typically focused on digital characteristics, with letters/strokes representing the information acquired from the current research in the integration of individual writing habits/styles. Previous studies have indicated that a word's attributes contribute to greater recognition than the attributes of a character or stroke. As a result of the complexity of Arabic handwriting, segmenting and separating letters and strokes from a script poses a challenge in addition to WI schemes. In this work, we propose new texture features for WI based on text. The histogram of oriented gradient (HOG) features are modified to extract good features on the basis of the histogram of the orientation for different angles of texts. The fusion of these features with the features of convolutional neural networks (CNNs) results in a good vector of powerful features. Then, we reduce the features by selecting the best ones using a genetic algorithm. The normalization method is used to normalize the features and feed them to an artificial neural network classifier. Experimental results show that the proposed augmenter enhances the results for HOG features and ResNet50, as well as the proposed model, because the amount of data is increased. Such a large data volume helps the system to retrieve extensive information about the nature of writing patterns. The affective result of the proposed model for whole paragraphs, lines, and sub words is obtained using different models and then compared with those of the CNN and ResNet50. The whole paragraphs produce the best results in all models because they contain rich information and the model can utilize numerous features for different words. The HOG and CNN features achieve 94.2% accuracy for whole paragraphs with augmentation, 83.2% of accuracy for lines, and 78% accuracy for sub words. Thus, this work provides a system that can identify writers on the basis of their handwriting and builds a powerful model that can help identify writers on the basis of their sentences, words, and sub words.

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Arabic Artistic Script Style Identification Using Texture Descriptors.

Cited by 3 publications

References 22 publications

Utilizing Deep Learning in Arabic Text Classification Sentiment Analysis of Twitter

Utilizing Deep Learning in Arabic Text Classification Sentiment Analysis of Twitter

Fast learning neural network based on texture for Arabic calligraphy identification

Hybrid Trainable System for Writer Identification of Arabic Handwriting

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