Character Recognition of Arabic Handwritten Characters Using Deep Learning

Jbrail, Mohammed Widad

doi:10.53898/josse2022213

Cited by 5 publications

(2 citation statements)

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“…Jbrail et al [129] developed four CNNs architectures to recognize isolated handwritten Arabic characters. The architectures use a different number of layers (3, 9, 13 layers).…”

Section: Figure 28: Sample Pictures From Aptid/mf Datasetmentioning

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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“…Jbrail et al [129] developed four CNNs architectures to recognize isolated handwritten Arabic characters. The architectures use a different number of layers (3, 9, 13 layers).…”

Section: Figure 28: Sample Pictures From Aptid/mf Datasetmentioning

confidence: 99%

Arabic Optical Character Recognition: A Review

Alghyaline¹

2023

Computer Modeling in Engineering &Amp; Sciences

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“…Character datasets such as Arabic [9][10][11][12] and Latin [4,13,14] were created from different alphabets and languages to be used in handwriting character recognition. There are handwritten character recognition studies on many languages and alphabets in the literature.…”

Section: Related Workmentioning

confidence: 99%

Comparison of Handwritten Recognition Methods on Arabic and Latin Characters

Tutar

2022

J. Stud. Sci. Eng.

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In this article, both machine learning techniques and deep learning methods were applied on the digit datasets created using the Arabic and Latin alphabets, and the performances of the methods were compared. Each method was tested with various parameters and the results were analyzed. In addition, with this study, the recognizability of handwritten numeral datasets created using different alphabets was also observed. For experiments, an Arabic alphabet handwritten digit dataset (60,000 training and 10,000 testings) and a Latin alphabet handwritten digit dataset (60,000 training and 10,000 testings) were used. When the results of the experiment are examined, it is seen that successful results are obtained in the classification made with the MADBase dataset in some methods and in the classification made with the MNIST dataset in some methods. As a result, it can be stated that the handwriting character recognition success of a method cannot be measured only by the classification made on a dataset. Also, the digits written in the Arabic alphabet appear to be almost more recognizable than the digits written in the Latin alphabet.

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