Synthesizing versus Augmentation for Arabic Word Recognition with Convolutional Neural Networks

Alaasam, Reem; Barakat, Berat Kurar; El-Sana, Jihad

doi:10.1109/asar.2018.8480189

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…In handwriting recognition, acquiring large and diverse datasets can be extremely difficult. Traditional data augmentation techniques involve geometric transformations and image manipulations such as rotation, scaling, skewing, and shifting of images, introducing variability in orientation, size and position, thereby mimicking the natural variations observed in handwritten text [32]- [38]. Elastic deformations, achieved through random warping or jittering of the image, simulate distortions and imperfections typically found in real-world documents.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing Arabic Handwritten Recognition System-Based CNN-BLSTM Using Generative Adversarial Networks

Rabi,

Amrouche

2024

EJAI

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Arabic Handwritten Recognition (AHR) presents unique challenges due to the complexity of Arabic script and the limited availability of training data. This paper proposes an approach that integrates generative adversarial networks (GANs) for data augmentation within a robust CNN-BLSTM architecture, aiming to significantly improve AHR performance. We employ a CNN-BLSTM network coupled with connectionist temporal classification (CTC) for accurate sequence modeling and recognition. To address data limitations, we incorporate a GANs based data augmentation module trained on the IFN-ENIT Arabic handwriting dataset to generate realistic and diverse synthetic samples, effectively augmenting the original training corpus. Extensive evaluations on the IFN-ENIT benchmark demonstrate the efficacy of adopted approach. We achieve a recognition rate of 95.23%, surpassing the baseline model by 3.54%. This research presents a promising approach to data augmentation in AHR and demonstrates a significant improvement in word recognition accuracy, paving the way for more robust and accurate AHR systems.

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Section: Literature Reviewmentioning

confidence: 99%

Enhancing Arabic Handwritten Recognition System-Based CNN-BLSTM Using Generative Adversarial Networks

Rabi,

Amrouche

2024

EJAI

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“…The network comprises 2 convolutional and 3 fully connected layers and reports a recognition rate of 81%. The work was later extended in [8] to study the impact of synthesizing and augmenting data to recognize 39 subword classes from 10 pages of the VML-HD dataset. Through a comprehensive series of experiments, the authors concluded that data augmentation results in relatively better performance as compared to synthesizing data.…”

Section: Handwriting Recognition In Historical Arabic Documentsmentioning

confidence: 99%

Subword Recognition in Historical Arabic Documents using C-GRUs

Hassen¹,

Al-Madeed²,

Bouridane³

2021

TEM Journal

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The recent years have witnessed an increased tendency to digitize historical manuscripts that not only ensures the preservation of these collections but also allows researchers and end-users’ direct access to these images. Recognition of Arabic handwriting is challenging due to the highly cursive nature of the script and other challenges associated with historical documents (degradation etc.). This paper presents an end-to-end system to recognize Arabic handwritten sub words in historical documents. More specifically, we introduce a hybrid CNN-GRU model where the shallow convolutional network learns robust feature representations while the GRU layers carry out the sequence modelling and generate the transcription of the text. The proposed system is evaluated on two different datasets, IBN SINA and VML-HD reporting recognition rates of 96.10% and 98.60% respectively. A comparison with existing techniques evaluated on the same datasets validates the effectiveness of our proposed model in characterizing Arabic subwords.

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New Transformer Approach to the Recognition of Mediaeval Arabic Historical Manuscripts

Maouloud,

2024

Lecture Notes in Networks and Systems

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Synthesizing versus Augmentation for Arabic Word Recognition with Convolutional Neural Networks

Cited by 4 publications

References 26 publications

Enhancing Arabic Handwritten Recognition System-Based CNN-BLSTM Using Generative Adversarial Networks

Enhancing Arabic Handwritten Recognition System-Based CNN-BLSTM Using Generative Adversarial Networks

Subword Recognition in Historical Arabic Documents using C-GRUs

New Transformer Approach to the Recognition of Mediaeval Arabic Historical Manuscripts

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