Improved Arabic characters recognition by combining multiple machine learning classifiers

Alabbas, Maytham; Khudeyer, Raidah S.; Jaf, Sardar

doi:10.1109/ialp.2016.7875982

Cited by 6 publications

(5 citation statements)

References 7 publications

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“…According to the original ASM, the dehazing picture J(x) is presented in (1). The AOD-Net experiment demonstrated that dependable dehazing performance may be achieved without separately estimating t(x) and A.…”

Section: Dehaze-netmentioning

confidence: 99%

“…Finally, we will briefly summarize the advantages and disadvantages of each of the methods used to remove fog in the following Table (1).…”

Section: 𝐽(𝑥) =mentioning

confidence: 99%

“…Machine learning is as versatile as character recognition and categorization [ 1 ] . And with the tremendous development taking place using deep learning in the medical field in particular, and the techniques of society in general [2,3].…”

mentioning

confidence: 99%

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A Review of Methods of Removing Haze from An Image

Karoon

Nemer

2022

IJEER

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A literature review aids in comprehending and gaining further information about a certain area of a subject. The presence of haze, fog, smoke, rain, and other harsh weather conditions affects outdoor photos. Images taken in unnatural weather have weak contrast and poor colors. This may make detecting objects in the produced hazy pictures difficult. In computer vision, scenes and images taken in a foggy atmosphere suffer from blurring. This work covers a study of many remove haze algorithms for eliminating haze collected in real-world weather scenarios in order to recover haze-free images rapidly and with improved quality. The contrast, viewing range, and color accuracy have been enhanced. All of these techniques it is used in countless fields. Some of the applications that use this technology outdoor surveillance, object recognition, underwater photography, and so on.

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Section: Dehaze-netmentioning

confidence: 99%

“…Finally, we will briefly summarize the advantages and disadvantages of each of the methods used to remove fog in the following Table (1).…”

Section: 𝐽(𝑥) =mentioning

confidence: 99%

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A Review of Methods of Removing Haze from An Image

Karoon

Nemer

2022

IJEER

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“…The combination of one or more systems is a popular way of improving accuracy in different tasks, where the new system performs the same task to exploit the unique advantage of each system and reduce some of the random errors [8]. In this work, we combine two commonly used Machine Learning (ML) algorithms: RF and SVM algorithm with CNN which are used for the classification task.…”

Section: Introductionmentioning

confidence: 99%

Combination of machine learning algorithms and Resnet50 for Arabic Handwritten Classification

Khudeyer¹,

Almoosawi

2023

IJCAI

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The recognition or classification of Arabic handwritten characters is extremely crucial in many applications and, at the same time, one of the biggest challenges that machine learning faces. The emergence of deep learning, particularly Convolutional Neural Networks (CNN), is considered a suitable technique to face these challenges. In this research paper, an investigation model is proposed to make recognition for Arabic handwriting utilizing one of CNN architectures: ResNet50 architecture, after replacing the last layer with one of two types of machine learning algorithms, Support Vector Machine (SVM) and Random Forest (RF), to reduce training time and increase overall accuracy. Our experimental work was performed on three data sets: Arabic Handwritten Character Dataset (AHCD), Alexa Isolated Alphabet Dataset (AIA9K), and Hijja Dataset. Experimental results show that combining ResNet50 with random forest produces more accurate and consistent results than the ResNet50 model produces by itself. Finally, the comparison with the other methods across all data sets demonstrates the robustness and effectiveness of the combination of random forest with the ResNet50 approach. Where the modified ResNet50 architecture has achieved a rate of 92.37%, 98.39%, and 91.64%, while the combination architecture has achieved 95%, 99%, and 92.4% for AIA9K, AHCD, and Hijja datasets, respectively. Povzetek: Avtorji so razvili novo metodo kot nadgradnjo Resnet50 z dodatkom zadnjega nivoja v obliki SVM in RF. Na več domenah je dosegla boljše rezultate kot osnovni Resnet50.

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“…[1,2]. Arabic Handwritten Recognition (AHR) has been successfully achieved using a variety of machine learning (ML) techniques [8]. However, deep Learning (DL) architectures can considerably enhance AHR technology.…”

Section: Introductionmentioning

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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Improved Arabic characters recognition by combining multiple machine learning classifiers

Cited by 6 publications

References 7 publications

A Review of Methods of Removing Haze from An Image

A Review of Methods of Removing Haze from An Image

Combination of machine learning algorithms and Resnet50 for Arabic Handwritten Classification

Arabic Handwritten Character Recognition Using Convolutional Neural Networks

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