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

Eltay, Mohamed; Zidouri, Abdelmalek; Ahmad, Irfan; Elarian, Yousef

doi:10.1007/978-3-030-86198-8_23

Cited by 4 publications

(7 citation statements)

References 28 publications

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“…As shown in Table 3, the different recognition levels found in the studies are displayed. In general, the most utilized recognition level was at word level, followed by 5.00 A study of data augmentation for handwritten character recognition using deep learning [55] 5.00 Generative adversarial network based adaptive data augmentation for handwritten Arabic text recognition [56] 5.00 Improving Handwritten Arabic Text Recognition Using an Adaptive Data Augmentation Algorithm [57] 5.00 AFFGANwriting: A Handwriting Image Generation Method Based on Multi-feature Fusion [58] 5.00 text-line level. This indicates that these two levels share similar challenges, where data augmentation applied to words can expand to text lines, and data augmentation applied to text lines can contract to words.…”

Section: Recognition Tasks and Datasetsmentioning

confidence: 99%

“…OpenHaRT [66], boasting a large database of approximately 710,000 images, was utilized in the study of [46]. The dataset from the "Institute for Communications Technology/Ecole Nationale d'Ingénieurs de Tunis" (IFN/ENIT) [67], employed in the studies conducted by [35,56,57], offers character and word recognition capabilities and encompasses around 411 different writers. The "Arabic Handwriting Data Base" (AHDB) [68], used in the studies conducted by [56,57], consists of characters and words derived from numerical values and bank check filling.…”

Section: Recognition Tasks and Datasetsmentioning

confidence: 99%

“…The dataset from the "Institute for Communications Technology/Ecole Nationale d'Ingénieurs de Tunis" (IFN/ENIT) [67], employed in the studies conducted by [35,56,57], offers character and word recognition capabilities and encompasses around 411 different writers. The "Arabic Handwriting Data Base" (AHDB) [68], used in the studies conducted by [56,57], consists of characters and words derived from numerical values and bank check filling. Finally, the "Multilingual Automatic Document Classification Analysis and Translation" (MADCAT) database [69][70][71] was used by the work [51], and consists of handwritten Arabic documents scanned at high resolution, totaling 750,000 images of segmented lines.…”

Section: Recognition Tasks and Datasetsmentioning

confidence: 99%

“…The Digital Image Processing (DIP) involves algorithms to apply transformations to digital images. DIP is a traditional approach to data augmentation in the field of offline handwritten text recognition research, and its algorithms are widely used with optical models [30,34,35,42,50,54,55,57]. This allows to apply randomness in text images transformations, which in turn helps to prolong the training process of optical models and prevent premature overfitting.…”

Section: Digital Image Processingmentioning

confidence: 99%

“…The work is accessible in a public repository. 7 Eltay et al [57] proposed a data augmentation method based on the frequency distribution of characters across the dataset. In this way, the method gives more weight to less frequent characters in a word, aiming to balance the character distribution across the dataset.…”

Section: Digital Image Processingmentioning

confidence: 99%

See 4 more Smart Citations

Data Augmentation for Offline Handwritten Text Recognition: A Systematic Literature Review

de Sousa Neto,

Bezerra,

de Moura

et al. 2024

SN COMPUT. SCI.

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Offline Handwritten Text Recognition (HTR) systems concern the automatic recognition and transcription of handwritten text from scanned images to digital media. Recently, HTR research field has become increasingly important due to the growing need for digitizing documents and automating data entry across various industries. However, achieving satisfactory results depend on the amount of available samples to train an optical model. Creating and labeling large enough datasets for this purpose often require significant time and effort, that in some situations may be impractical. To address this problem, data augmentation approaches are commonly used as an essential component of HTR systems. In this way, the present work aims to identify, explore, and analyze the scope of data augmentation approaches for offline HTR systems. Furthermore, we detailed our research protocol and answered four pertinent research questions, which enabled us to discuss trends and possible gaps. A search was conducted across five scientific databases, focusing on papers published between 2012 and 2023. The search yielded 976 primary papers, with 32 meeting the criteria for inclusion in this review. Our results indicate that handwriting synthesis is an emerging research field, and we observed that Digital Image Processing (DIP) is still widely used as an image generator. Nevertheless, the application of Generative Adversarial Networks (GAN) has gained traction in recent years owing to its impressive ability to synthesize images of handwritten text with arbitrary style and content. In addition, we explored and analyzed the most commonly used datasets and text recognition levels in the selected works.

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