Methods of data augmentation for palimpsest character recognition with Deep Neural Network

Starynska, Anna; Easton, Roger L.; Messinger, David W.

doi:10.1145/3151509.3151515

Cited by 4 publications

(4 citation statements)

References 9 publications

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“…Ref. [20] introduced several data augmentation techniques for improving palimpsest character recognition using deep neural networks. Palimpsests are manuscripts with overlaid text that makes recognizing the underlying characters challenging.…”

Section: Related Workmentioning

confidence: 99%

Generative Adversarial Network Models for Augmenting Digit and Character Datasets Embedded in Standard Markings on Ship Bodies

Abdulraheem,

Suleiman,

Jung

2023

Electronics

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Accurate recognition of characters imprinted on ship bodies is essential for ensuring operational efficiency, safety, and security in the maritime industry. However, the limited availability of datasets of specialized digits and characters poses a challenge. To overcome this challenge, we propose a generative adversarial network (GAN) model for augmenting the limited dataset of special digits and characters in ship markings. We evaluated the performance of various GAN models, and the Wasserstein GAN with Gradient Penalty (WGAN-GP) and Wasserstein GAN with divergence (WGANDIV) models demonstrated exceptional performance in generating high-quality synthetic images that closely resemble the original imprinted characters required for augmenting the limited datasets. And the evaluation metric, Fréchet inception distance, further validated the outstanding performance of the WGAN-GP and WGANDIV models, establishing them as optimal choices for dataset augmentation to enhance the accuracy and reliability of recognition systems.

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Section: Related Workmentioning

confidence: 99%

Generative Adversarial Network Models for Augmenting Digit and Character Datasets Embedded in Standard Markings on Ship Bodies

Abdulraheem,

Suleiman,

Jung

2023

Electronics

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“…The authors investigated the advantages of training a machine learning classifier with examples that were artificially manufactured to supplement the data. In addition, in [38], the authors demonstrated the method of training a deep neural network (DNN) for optical character recognition (OCR) on historical manuscripts with a small amount of data. They examined various methods for data augmentation of palimpsests and evaluated the impact of various techniques on the performance of the DNN.…”

Section: Data Augmentation By Ganmentioning

confidence: 99%

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

Abdulraheem

Jung

2022

Sustainability

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In cases where an efficient information retrieval (IR) system retrieves information from images with engraved digits, as found on medicines, creams, ointments, and gels in squeeze tubes, the system needs to be trained on a large dataset. One of the system applications is to automatically retrieve the expiry date to ascertain the efficacy of the medicine. For expiry dates expressed in engraved digits, it is difficult to collect the digit images. In our study, we evaluated the augmentation performance for a limited, engraved-digit dataset using various generative adversarial networks (GANs). Our study contributes to the choice of an effective GAN for engraved-digit image data augmentation. We conclude that Wasserstein GAN with a gradient norm penalty (WGAN-GP) is a suitable data augmentation technique to address the challenge of producing a large, realistic, but synthetic dataset. Our results show that the stability of WGAN-GP aids in the production of high-quality data with an average Fréchet inception distance (FID) value of 1.5298 across images of 10 digits (0–9) that are nearly indistinguishable from our original dataset.

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“…In practice historical palimpsest have very uneven background, and one of the biggest challenge lay in separating of under-text from background. Some of the methods (see [36,44]) tried to perform automatic and semi-automatic text transcription directly on the palimpsest text processed by PCA. However, this method requires labeling of some portion of the same document, since the appearance of the under-text and mixing of components is usually unique to a particular manuscript.…”

Section: Palimpsest Text Separationmentioning

confidence: 99%

“…Archimedes dataset The Archimedes dataset is the only known palimpsest character dataset with ground truth, and was created as a part of previous work [36]. It was created from the scans of the Archimedes palimpsest that has two layers of text.…”

Section: Datasetsmentioning

confidence: 99%

Revealing a history: palimpsest text separation with generative networks

Starynska

Messinger

Kong

2021

IJDAR

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A palimpsest is a historical manuscript in which the original text (termed under-text) was erased and overwritten with another script in order to recycle the parchment. One of the main challenges in studying palimpsests is to reveal the under-text. Due to the development of multi-spectral imaging, the original text can sometimes be recovered through material differences of inks and parchment (Easton et al., in: 2011 19th European signal processing conference, IEEE, 2011). However, generally, the revealed text can be observed only partially due to the overlap with newer text and degradation of the material. In this work, we propose revealing the under-text completely using deep generative networks, by leveraging prior spatial information of the under-text script. To optimize the under-text, we mimic the process of palimpsest creation. This is done by generating the under-text from a separately trained generative network to match it to the palimpsest image after mixing it with foreground text. The mixing process is represented by a separate neural network, that is optimized with the under-text image to match the original palimpsest. We also add an additional background generative network to compensate for the unevenness of the background. We propose a novel way of training the background generative network, that does not require isolated background samples and can use any region with layers of text. This paper illustrates the first known attempt to solve palimpsest text layer separation with deep generative networks. We evaluate our method performance on artificial and real palimpsest manuscripts by measuring character recognition and pixel-wise accuracy of the reconstructed under-text.

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Methods of data augmentation for palimpsest character recognition with Deep Neural Network

Cited by 4 publications

References 9 publications

Generative Adversarial Network Models for Augmenting Digit and Character Datasets Embedded in Standard Markings on Ship Bodies

Generative Adversarial Network Models for Augmenting Digit and Character Datasets Embedded in Standard Markings on Ship Bodies

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

Revealing a history: palimpsest text separation with generative networks

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