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

Holistic Recognition

Local Binary Pattern

Support Vector Machines

Artificial Neural Network.

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.

AL-Shatnawi, Atallah

Al-Saqqar, Faisal

Alhusban, Safa’a

International Journal of Interactive Mobile Technologies (iJIM); Vol. 14 No. 16 (2020); pp. 20-34

1865-7923

International Association of Online Engineering (IAOE), Vienna, Austria

2020-09-22

Copyright (c) 2020 Atallah AL-Shatnawi, Faisal Al-Saqqar, Safa’a alhusban

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eng

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Peer-reviewed Article