A Hybrid Deep Model for Recognizing Arabic Handwritten Characters

Alrobah, Naseem; Albahli, Saleh

doi:10.1109/access.2021.3087647

Cited by 31 publications

(19 citation statements)

References 24 publications

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“…Each layer is followed by a rectified linear unit (ReLU) [38]. This is followed by an average-pooling layer and a dense layer [39]. There are also two normalization layers, two fully connected layers, and a softmax layer.…”

Section: Alexnetmentioning

confidence: 99%

Big Data Analytics: Deep Content-Based Prediction with Sampling Perspective

Albattah¹,

Albahli²

2023

Computer Systems Science and Engineering

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The world of information technology is more than ever being flooded with huge amounts of data, nearly 2.5 quintillion bytes every day. This large stream of data is called big data, and the amount is increasing each day. This research uses a technique called sampling, which selects a representative subset of the data points, manipulates and analyzes this subset to identify patterns and trends in the larger dataset being examined, and finally, creates models. Sampling uses a small proportion of the original data for analysis and model training, so that it is relatively faster while maintaining data integrity and achieving accurate results. Two deep neural networks, AlexNet and DenseNet, were used in this research to test two sampling techniques, namely sampling with replacement and reservoir sampling. The dataset used for this research was divided into three classes: acceptable, flagged as easy, and flagged as hard. The base models were trained with the whole dataset, whereas the other models were trained on 50% of the original dataset. There were four combinations of model and sampling technique. The F-measure for the AlexNet model was 0.807 while that for the Den-seNet model was 0.808. Combination 1 was the AlexNet model and sampling with replacement, achieving an average F-measure of 0.8852. Combination 3 was the AlexNet model and reservoir sampling. It had an average F-measure of 0.8545. Combination 2 was the DenseNet model and sampling with replacement, achieving an average F-measure of 0.8017. Finally, combination 4 was the DenseNet model and reservoir sampling. It had an average F-measure of 0.8111. Overall, we conclude that both models trained on a sampled dataset gave equal or better results compared to the base models, which used the whole dataset.

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Section: Alexnetmentioning

confidence: 99%

Big Data Analytics: Deep Content-Based Prediction with Sampling Perspective

Albattah¹,

Albahli²

2023

Computer Systems Science and Engineering

Self Cite

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“…Using 29 classes that segregated forms from the Hijja dataset, NASEEM ALROBAH AND SALEH ALBAHLI [12]created a hybridized architecture in 2020 with the goal of classifying Arabic handwriting characteristics. Feature extraction utilizing two architectures is the initial stage.…”

Section: Related Workmentioning

confidence: 99%

Optimizing Handwritten Arabic Character Recognition: Feature Extraction, Concatenation, and PSO-Based Feature Selection.

Rouabhi,

Belbachir,

Tlemsani

2023

Preprint

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Recognizing and writing Arabic characters correctly is a difficult task, especially for non-native speakers. With the growing trend toward digital teaching and distance learning, there is a need for efficient and accurate automatic recognition of Arabic characters. The work investigates three distinct approaches to address this challenge. Firstly, features are extracted from handwritten Arabic character images using two pre-trained models: EfficientNet B2 and DenseNet 201. These extracted features are then employed for classification utilizing various classifiers. Secondly, the features obtained from both models are concatenated to enhance classification performance by leveraging the unique representations learned by each model. Lastly, the Particle Swarm Optimization (PSO) algorithm is integrated into the feature selection process, identifying the most relevant features that contribute to improved classification accuracy. The best test accuracies obtained were 85.94% in the first experiment, 88.73% in the second, and 90.20% in the third.

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“…In residual networks, some weights from previous layers are added to a later layer bypassing layer in between. These bypassing layers contain weights influenced by immediately preceding layers as well as from layers from deeper layers [40]. This solved the vanishing gradient problem and training the deep neural network was feasible.…”

Section: Modelsmentioning

confidence: 99%

Transfer Learning on Deep Neural Networks to Detect Pornography

Albahli¹

2022

Computer Systems Science and Engineering

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While the internet has a lot of positive impact on society, there are negative components. Accessible to everyone through online platforms, pornography is, inducing psychological and health related issues among people of all ages. While a difficult task, detecting pornography can be the important step in determining the porn and adult content in a video. In this paper, an architecture is proposed which yielded high scores for both training and testing. This dataset was produced from 190 videos, yielding more than 19 h of videos. The main sources for the content were from YouTube, movies, torrent, and websites that hosts both pornographic and non-pornographic contents. The videos were from different ethnicities and skin color which ensures the models can detect any kind of video. A VGG16, Inception V3 and Resnet 50 models were initially trained to detect these pornographic images but failed to achieve a high testing accuracy with accuracies of 0.49, 0.49 and 0.78 respectively. Finally, utilizing transfer learning, a convolutional neural network was designed and yielded an accuracy of 0.98.

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A Hybrid Deep Model for Recognizing Arabic Handwritten Characters

Cited by 31 publications

References 24 publications

Big Data Analytics: Deep Content-Based Prediction with Sampling Perspective

Big Data Analytics: Deep Content-Based Prediction with Sampling Perspective

Optimizing Handwritten Arabic Character Recognition: Feature Extraction, Concatenation, and PSO-Based Feature Selection.

Transfer Learning on Deep Neural Networks to Detect Pornography

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