A review of feature extraction techniques for handwritten Arabic text recognition

2015 ◽  
Author(s):  
Bouchra El Qacimy ◽  
Ahmed Hammouch ◽  
Mounir Ait Kerroum
Keyword(s):  
Feature Extraction ◽  
Text Recognition ◽  
Arabic Text ◽  
Extraction Techniques ◽  
Handwritten Arabic

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

2020 ◽  
Vol 14 (16) ◽  
pp. 20
Author(s):  
Atallah AL-Shatnawi ◽  
Faisal Al-Saqqar ◽  
Safa’a Alhusban
Keyword(s):  
Local Binary Pattern ◽  
Text Recognition ◽  
Support Vector ◽  
Svm Classifier ◽  
Arabic Text ◽  
Training Methods ◽  
Learning Approaches ◽  
Suggested Model ◽  
Holistic Model ◽  
Handwritten Arabic

<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>

Improving Handwritten Arabic Text Recognition Using an Adaptive Data-Augmentation Algorithm

2021 ◽  
pp. 322-335
Author(s):  
Mohamed Eltay ◽  
Abdelmalek Zidouri ◽  
Irfan Ahmad ◽  
Yousef Elarian
Keyword(s):  
Data Augmentation ◽  
Text Recognition ◽  
Arabic Text ◽  
Augmentation Algorithm ◽  
Handwritten Arabic
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