Recognition of Isolated Handwritten Arabic Characters

Almansari, Osamah Abdulrahman; Hashim, Nik Nur Wahidah Nik

doi:10.1109/icom47790.2019.8952035

Cited by 12 publications

(8 citation statements)

References 7 publications

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“…But the authors achieved this result using training and testing sets with no validation set, so the overfitting not taken in considerable. The lowest accuracies were 95.3% in [25], 97% in [23] and 97.2% in [21].…”

Section: Resultsmentioning

confidence: 89%

Deep ensemble neural networks for recognizing isolated Arabic handwritten characters

Alyahya¹,

Ismail²,

Al-Salman³

2020

TIPCV

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In recent years, handwritten character recognition has become an active research field. In particular, digitalization has triggered the interest of researchers from various computing disciplines to address several handwriting related challenges. Despite these efforts, there are still opportunities for the development and improvement of the recognition of the handwritten Arabic letters. In this paper, we designed and developed a deep ensemble architecture in which ResNet-18 architecture is exploited to model and classify character images. Specifically, we adapted ResNet-18 by adding a dropout layer after all convolutional layer and integrated it in multiple ensemble models to automatically recognize isolated handwritten Arabic characters. A standard Arabic Handwritten Character Dataset (AHCD) was used in the experiments to train and assess all the proposed models. Satisfactory results were obtained using all models. The best-attained accuracy was 98.30% using a typical ResNet-18 model. Similarly, 98.00% and 98.03% accuracies were obtained using an ensemble model with one fully connected layer (1 FC) and an ensemble with two fully connected layers (2 FC) coupled with a dropout layer, respectively.

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Section: Resultsmentioning

confidence: 89%

Deep ensemble neural networks for recognizing isolated Arabic handwritten characters

Alyahya¹,

Ismail²,

Al-Salman³

2020

TIPCV

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“…In the study conducted by Almansari and Hashim [49], various hyperparameters were tested on a simple CNN architecture to achieve optimal performance. The authors experimented with different batch sizes, training epochs, filter sizes, and dropout values.…”

Section: Simple Custom-designed Architecturesmentioning

confidence: 99%

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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“…They modified several of the CNN's parameters to increase Arabic character recognition, and as a result, they reached an accuracy of 97.2 percent. Almansari et al [14]They want to create a model of a deep learning architecture in Python by integrating a multilayer perceptron (MLP) neural network with a convolutional neural network (CNN). This is the emphasis of the project that they have presented.…”

Section: Motivationmentioning

confidence: 99%

Arabic Handwritten Character Recognition Using Convolutional Neural Networks

Alsayed,

Li,

Fat’hAlalim

et al. 2023

Preprint

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The Arabic language is one of the six most important languages in the world. Because more than 420 million people worldwide use the Arabic script, research into the recognition of Arabic handwriting is crucial. The demand for software that can automatically read and interpret Arabic Handwriting has been rapidly expanding in recent years as the use of digital devices has become increasingly widespread. Characters are written by Hands in Arabic are more difficult to decipher than those noted in English or other languages because of the nature of the words used in Arabic. In this study, we designed a new model, Convolutional Neural Network 14 Layers (CNN-14), to recog-nise handwritten Arabic characters. The The model was trained and tested on the Arabic Handwritten Character Dataset (AHCD) and Hijja datasets, The proposed model achieved good results, with an accuracy of 99.36 per cent in AHCD and 94.35 per cent Hijja dataset.

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Recognition of Isolated Handwritten Arabic Characters

Cited by 12 publications

References 7 publications

Deep ensemble neural networks for recognizing isolated Arabic handwritten characters

Deep ensemble neural networks for recognizing isolated Arabic handwritten characters

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

Arabic Handwritten Character Recognition Using Convolutional Neural Networks

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