A comparative study of multi-class support vector machine methods for Arabic characters recognition

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

doi:10.1109/aiccsa.2015.7507240

Cited by 12 publications

(2 citation statements)

References 9 publications

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“…Thereafter, the SVM can determine the hyperplane with the best separation [13]. In this paper, multi-class SVM classifiers were employed for the text images classification using the following SVM kernel functions [19]: Artificial Neural Network (ANN): Artificial Neural Network (ANN) is considered as a part of a computing system that is designed to mimic the way of analyzing and processing information by the human brain. Furthermore, an ANN model can be seen as a set of interconnected nodes which communicate together and with the outside using a well-known connections called synapses [20].…”

Section: Text Classificationmentioning

confidence: 99%

A Holistic Model for Recognition of Handwritten Arabic Text Based on the Local Binary Pattern Technique

AL-Shatnawi

Al-Saqqar

Alhusban

2020

Int. J. Interact. Mob. Technol.

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<p class="0abstract">In this paper, we introduce a multi-stage offline holistic handwritten Arabic text recognition model using the Local Binary Pattern (LBP) technique and two machine-learning approaches; Support Vector Machines (SVM) and Artificial Neural Network (ANN). In this model, the LBP method is utilized for extracting the global text features without text segmentation. The suggested model was tested and utilized on version II of the IFN/ENIT database applying the polynomial, linear, and Gaussian SVM and ANN classifiers. Performance of the ANN was assessed using the Levenberg-Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG) training methods. The classification outputs of the herein suggested model were compared and verified with the results obtained from two benchmark Arabic text recognition models (ATRSs) that are based on the Discrete Cosine Transform (DCT) and Principal Component Analysis (PCA) methods using various normalization sizes of images of Arabic text. The classification outcomes of the suggested model are promising and better than the outcomes of the examined benchmarks models. The best classification accuracies of the suggested model (97.46% and 94.92%) are obtained using the polynomial SVM classifier and the BR ANN training methods, respectively.</p>

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Section: Text Classificationmentioning

confidence: 99%

A Holistic Model for Recognition of Handwritten Arabic Text Based on the Local Binary Pattern Technique

AL-Shatnawi

Al-Saqqar

Alhusban

2020

Int. J. Interact. Mob. Technol.

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“…In classification in the present study, the multi-class polynomial, Gaussian, and linear SVM classifiers were used for classification of images of handwritten Arabic text by using the sequent SVM kernel functions [22]: prepare the data of the text image using the following two preliminary steps: -Extract the text skeleton using the skeletonizationbased morphological operation method, -Normalize the text image size to a suitable size, Extract the global text features using PCA, and then classify the text images using the Gaussian, linear, and polynomial SVM classifiers, end}…”

Section: E Classification Using Svm Classifiersmentioning

confidence: 99%

Handwritten Arabic Text Recognition using Principal Component Analysis and Support Vector Machines

Al-Saqqar¹,

AL-Shatnawi²,

Al‐diabat³

et al. 2019

IJACSA

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In this paper, an offline holistic handwritten Arabic text recognition system based on Principal Component Analysis (PCA) and Support Vector Machine (SVM) classifiers is proposed. The proposed system consists of three primary stages: preliminary processing, feature extraction using PCA, and classification using the polynomial, linear, and Gaussian SVM classifiers. In this proposed system, text skeleton is first extracted and the images of the text are normalized into uniform size for extraction of the global features of the Arabic words using PCA. Recognition performance of this proposed system was evaluated on version 2 of the IFN/ENIT database of handwritten Arabic text using the polynomial, linear, and Gaussian SVM classifiers. The classification results of the proposed system were compared with the results produced by a benchmark. TRS that is depending on the Discrete Cosine Transform (DCT) method using numerous normalization sizes of Arabic text images. The experimental testing results support the effectiveness of the proposed system in holistic recognition of the handwritten Arabic text.

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Structural and Statistical Feature Extraction Methodology for the Recognition of Handwritten Arabic Words

Amara¹,

Zidi

Ghédira³

2019

Hybrid Intelligent Systems

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A comparative study of multi-class support vector machine methods for Arabic characters recognition

Cited by 12 publications

References 9 publications

A Holistic Model for Recognition of Handwritten Arabic Text Based on the Local Binary Pattern Technique

A Holistic Model for Recognition of Handwritten Arabic Text Based on the Local Binary Pattern Technique

Handwritten Arabic Text Recognition using Principal Component Analysis and Support Vector Machines

Structural and Statistical Feature Extraction Methodology for the Recognition of Handwritten Arabic Words

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