A Comparative Study of Engraved-Digit Data Augmentation by Generative Adversarial Networks

Abdulraheem, Abdulkabir; Jung, Im Y.

doi:10.3390/su141912479

Cited by 5 publications

(6 citation statements)

References 38 publications

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“…GANs are powerful tools for data augmentation, because they can generate synthetic data that closely resemble real-world samples [1][2][3]. We explored the effectiveness of GAN-based augmentation techniques, such as Wasserstein GAN with a Gradient Penalty (WGAN-GP) and WGAN with Divergence (WGAN-DIV) [31], in improving the recognition performance of classifiers. The WGAN-GP and WGAN-DIV models, among several other GAN models, demonstrated exceptional performance during execution on the engraved digit dataset.…”

Section: Methodsmentioning

confidence: 99%

Enhancing the Automatic Recognition Accuracy of Imprinted Ship Characters by Using Machine Learning

Abdulraheem,

Suleiman,

Jung

2023

Sustainability

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In this paper, we address the challenge of ensuring safe operations and rescue efforts in emergency situations, for the sake of a sustainable marine environment. Our focus is on character recognition, specifically on deciphering characters present on the surface of aged and corroded ships, where the markings may have faded or become unclear over time, in contrast to vessels with clearly visible letters. Imprinted ship characters encompassing engraved, embroidered, and other variants found on ship components serve as vital markers for ship identification, maintenance, and safety in marine technology. The accurate recognition of these characters is essential for ensuring efficient operations and effective decision making. This study presents a machine-learning-based method that markedly improves the recognition accuracy of imprinted ship numbers and characters. This improvement is achieved by enhancing data classification accuracy through data augmentation. The effectiveness of the proposed method was validated by comparing it to State-of-the-Art classification technologies within the imprinted ship character dataset. We started with the originally sourced dataset and then systematically increased the dataset size, using the most suitable generative adversarial networks for our dataset. We compared the effectiveness of classic and convolutional neural network (CNN)-based classifiers to our classifier, a CNN-based classifier for imprinted ship characters (CNN-ISC). Notably, on the augmented dataset, our CNN-ISC model achieved impressive maximum recognition accuracy of 99.85% and 99.7% on alphabet and digit recognition, respectively. Overall, data augmentation markedly improved the recognition accuracy of ship digits and alphabets, with the proposed classification model outperforming other methods.

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Section: Methodsmentioning

confidence: 99%

Enhancing the Automatic Recognition Accuracy of Imprinted Ship Characters by Using Machine Learning

Abdulraheem,

Suleiman,

Jung

2023

Sustainability

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“…The addition of emotions to neutral faces to increase the number of underrepresented categories has also been performed. Previous studies have explored augmentation techniques and synthetic datasets have used GANs in various domains; for example, Abdulraheem et al [ 35 ], conducted a study in which they leveraged GAN models to develop additional datasets to offer correct results for the automatic recognition of expiration dates in photos. This type of recognition demands a significant number of data for learning purposes.…”

Section: Related Studiesmentioning

confidence: 99%

A Comparative Analysis of the Novel Conditional Deep Convolutional Neural Network Model, Using Conditional Deep Convolutional Generative Adversarial Network-Generated Synthetic and Augmented Brain Tumor Datasets for Image Classification

Onakpojeruo,

Mustapha,

Ozsahin

et al. 2024

Brain Sciences

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Disease prediction is greatly challenged by the scarcity of datasets and privacy concerns associated with real medical data. An approach that stands out to circumvent this hurdle is the use of synthetic data generated using Generative Adversarial Networks (GANs). GANs can increase data volume while generating synthetic datasets that have no direct link to personal information. This study pioneers the use of GANs to create synthetic datasets and datasets augmented using traditional augmentation techniques for our binary classification task. The primary aim of this research was to evaluate the performance of our novel Conditional Deep Convolutional Neural Network (C-DCNN) model in classifying brain tumors by leveraging these augmented and synthetic datasets. We utilized advanced GAN models, including Conditional Deep Convolutional Generative Adversarial Network (DCGAN), to produce synthetic data that retained essential characteristics of the original datasets while ensuring privacy protection. Our C-DCNN model was trained on both augmented and synthetic datasets, and its performance was benchmarked against state-of-the-art models such as ResNet50, VGG16, VGG19, and InceptionV3. The evaluation metrics demonstrated that our C-DCNN model achieved accuracy, precision, recall, and F1 scores of 99% on both synthetic and augmented images, outperforming the comparative models. The findings of this study highlight the potential of using GAN-generated synthetic data in enhancing the training of machine learning models for medical image classification, particularly in scenarios with limited data available. This approach not only improves model accuracy but also addresses privacy concerns, making it a viable solution for real-world clinical applications in disease prediction and diagnosis.

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“…Collecting a large volume of this dataset was a challenging task because finding a large number of high-quality and sufficiently varied engraved digits to train an efficient recognition model was difficult. We overcame this challenge by augmenting the datasets with images generated by a Wasserstein GAN with a gradient penalty (WGAN-GP) and a Wasserstein divergence for the GAN (WGAN-DIV) [48]. In [48], we conducted a comprehensive evaluation of various state-of-the-art GAN models on engraved digit datasets, including MMGAN, NSGAN, LSGAN, ACGAN, DCGAN, WGAN, WGAN-GP, WGAN-DIV, DRA-GAN, BEGAN, EBGAN, and VAE.…”

Section: Engraved Digit Datasetsmentioning

confidence: 99%

“…We overcame this challenge by augmenting the datasets with images generated by a Wasserstein GAN with a gradient penalty (WGAN-GP) and a Wasserstein divergence for the GAN (WGAN-DIV) [48]. In [48], we conducted a comprehensive evaluation of various state-of-the-art GAN models on engraved digit datasets, including MMGAN, NSGAN, LSGAN, ACGAN, DCGAN, WGAN, WGAN-GP, WGAN-DIV, DRA-GAN, BEGAN, EBGAN, and VAE. We assessed the performance of each GAN model by combining a visual inspection of the generated samples and a calculation of the Fréchet Inception Distance values.…”

Section: Engraved Digit Datasetsmentioning

confidence: 99%

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Effective Digital Technology Enabling Automatic Recognition of Special-Type Marking of Expiry Dates

Abdulraheem,

Jung

2023

Sustainability

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In this study, we present a machine-learning-based approach that focuses on the automatic retrieval of engraved expiry dates. We leverage generative adversarial networks by augmenting the dataset to enhance the classifier performance and propose a suitable convolutional neural network (CNN) model for this dataset referred to herein as the CNN for engraved digit (CNN-ED) model. Our evaluation encompasses a diverse range of supervised classifiers, including classic and deep learning models. Our proposed CNN-ED model remarkably achieves an exceptional accuracy, reaching a 99.88% peak with perfect precision for all digits. Our new model outperforms other CNN-based models in accuracy and precision. This work offers valuable insights into engraved digit recognition and provides potential implications for designing more accurate and efficient recognition models in various applications.

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A Comparative Study of Engraved-Digit Data Augmentation by Generative Adversarial Networks

Cited by 5 publications

References 38 publications

Enhancing the Automatic Recognition Accuracy of Imprinted Ship Characters by Using Machine Learning

Enhancing the Automatic Recognition Accuracy of Imprinted Ship Characters by Using Machine Learning

A Comparative Analysis of the Novel Conditional Deep Convolutional Neural Network Model, Using Conditional Deep Convolutional Generative Adversarial Network-Generated Synthetic and Augmented Brain Tumor Datasets for Image Classification

Effective Digital Technology Enabling Automatic Recognition of Special-Type Marking of Expiry Dates

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