Segmentation of Handwritten Arabic Graphemes Using a Directed Convolutional Neural Network and Mathematical Morphology Operations

Elkhayati, Mohsine; Elkettani, Youssfi; Mourchid, Mohammed

doi:10.1016/j.patcog.2021.108288

Cited by 16 publications

(5 citation statements)

References 22 publications

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“…The results highlight the substantial value added by the CNN model in this particular context. This same directed model has also been shown to be effective for grapheme segmentation in another study conducted by Elkhayati et al [40]. This model was integrated with two mathematical morphology operations-dilation and erosion-for enhanced performance.…”

Section: Segmentationmentioning

confidence: 91%

CNN-based Methods for Offline Arabic Handwriting Recognition: A Review

El Khayati,

Kich,

Taouil

2024

Neural Process Lett

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Arabic Handwriting Recognition (AHR) is a complex task involving the transformation of handwritten Arabic text from image format into machine-readable data, holding immense potential across various applications. Despite its significance, AHR encounters formidable challenges due to the intricate nature of Arabic script and the diverse array of handwriting styles. In recent years, Convolutional Neural Networks (CNNs) have emerged as a pivotal and promising solution to address these challenges, demonstrating remarkable performance and offering distinct advantages. However, the dominance of CNNs in AHR lacks a dedicated comprehensive review in the existing literature. This review article aims to bridge the existing gap by providing a comprehensive analysis of CNN-based methods in AHR. It covers both segmentation and recognition tasks, delving into advancements in network architectures, databases, training strategies, and employed methods. The article offers an in-depth comparison of these methods, considering their respective strengths and limitations. The findings of this review not only contribute to the current understanding of CNN applications in AHR but also pave the way for future research directions and improved practices, thereby enriching and advancing this critical domain. The review also aims to uncover genuine challenges in the domain, providing valuable insights for researchers and practitioners.

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Section: Segmentationmentioning

confidence: 91%

CNN-based Methods for Offline Arabic Handwriting Recognition: A Review

El Khayati,

Kich,

Taouil

2024

Neural Process Lett

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“…In this paper, the typhoon is firstly identified from the FY-4A AGRI thermal infrared data using the YOLOX neural network, then the typhoon eye is located using the morphological image processing algorithm [3] .…”

Section: Methodsmentioning

confidence: 99%

Typhoon Identification and Positioning Using the FY-4A AGRI Thermal Infrared Data

Jiang

2022

J. Phys.: Conf. Ser.

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This paper addresses typhoon identification and positioning by using the thermal infrared data acquired by the Advanced Geostationary Radiation Imager (AGRI) on the Chinese Fengyun 4A (FY-4A) satellite. First, a training dataset, a validation dataset, and a test dataset of typhoons in the West Pacific Ocean close to China are created from the FY-4A AGRI thermal infrared data. Then, the YOLOX neural network is configured and trained, in which an average precision (AP) of 33.2 is obtained for the test dataset. Finally, with the prior knowledge that the brightness temperature of the typhoon eye is higher than that of its surroundings, the typhoon eyes are located using the morphological image processing method. The results of typhoon eye positioning are generally consistent with the Optimal Path (OP) dataset of tropical cyclones created by the China Meteorology Administration (CMA), and the mean errors in latitude and longitude are 0.0391° and 0.0334°, respectively.

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“…Image profile Project, statistical and topological information are used to segment words into characters. Elkhayati et al [54] proposed an approach to segment Handwritten Arabic characters based on morphological operations (erosion and Dilation) and directed CNN architecture. The segmentation approach achieved 97.35% accuracy on IFN/ENIT dataset.…”

Section: Segmentationmentioning

confidence: 99%

Arabic Optical Character Recognition: A Review

Alghyaline¹

2023

Computer Modeling in Engineering &Amp; Sciences

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This study aims to review the latest contributions in Arabic Optical Character Recognition (OCR) during the last decade, which helps interested researchers know the existing techniques and extend or adapt them accordingly. The study describes the characteristics of the Arabic language, different types of OCR systems, different stages of the Arabic OCR system, the researcher's contributions in each step, and the evaluation metrics for OCR. The study reviews the existing datasets for the Arabic OCR and their characteristics. Additionally, this study implemented some preprocessing and segmentation stages of Arabic OCR. The study compares the performance of the existing methods in terms of recognition accuracy. In addition to researchers' OCR methods, commercial and open-source systems are used in the comparison. The Arabic language is morphologically rich and written cursive with dots and diacritics above and under the characters. Most of the existing approaches in the literature were evaluated on isolated characters or isolated words under a controlled environment, and few approaches were tested on pagelevel scripts. Some comparative studies show that the accuracy of the existing Arabic OCR commercial systems is low, under 75% for printed text, and further improvement is needed. Moreover, most of the current approaches are offline OCR systems, and there is no remarkable contribution to online OCR systems.

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Segmentation of Handwritten Arabic Graphemes Using a Directed Convolutional Neural Network and Mathematical Morphology Operations

Cited by 16 publications

References 22 publications

CNN-based Methods for Offline Arabic Handwriting Recognition: A Review

CNN-based Methods for Offline Arabic Handwriting Recognition: A Review

Typhoon Identification and Positioning Using the FY-4A AGRI Thermal Infrared Data

Arabic Optical Character Recognition: A Review

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