Class-Based Contextual Modeling for Handwritten Arabic Text Recognition

2016 ◽  
Author(s):  
Irfan Ahmad ◽  
Gernot A. Fink
Keyword(s):  
Text Recognition ◽  
Arabic Text ◽  
Contextual Modeling ◽  
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

A Deep Learning based Arabic Script Recognition System: Benchmark on KHAT

2020 ◽  
Vol 17 (3) ◽  
pp. 299-305 ◽  
Author(s):  
Riaz Ahmad ◽  
Saeeda Naz ◽  
Muhammad Afzal ◽  
Sheikh Rashid ◽  
Marcus Liwicki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Recognition System ◽  
Learning Approach ◽  
Arabic Text ◽  
Data Set ◽  
Processing Step ◽  
Handwritten Arabic

This paper presents a deep learning benchmark on a complex dataset known as KFUPM Handwritten Arabic TexT (KHATT). The KHATT data-set consists of complex patterns of handwritten Arabic text-lines. This paper contributes mainly in three aspects i.e., (1) pre-processing, (2) deep learning based approach, and (3) data-augmentation. The pre-processing step includes pruning of white extra spaces plus de-skewing the skewed text-lines. We deploy a deep learning approach based on Multi-Dimensional Long Short-Term Memory (MDLSTM) networks and Connectionist Temporal Classification (CTC). The MDLSTM has the advantage of scanning the Arabic text-lines in all directions (horizontal and vertical) to cover dots, diacritics, strokes and fine inflammation. The data-augmentation with a deep learning approach proves to achieve better and promising improvement in results by gaining 80.02% Character Recognition (CR) over 75.08% as baseline.

scholarly journals Deep Sparse Auto-Encoder Features Learning for Arabic Text Recognition

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 18569-18584
Author(s):  
Najoua Rahal ◽  
Maroua Tounsi ◽  
Amir Hussain ◽  
Adel M. Alimi
Keyword(s):  
Text Recognition ◽  
Arabic Text
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