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2021 | OriginalPaper | Buchkapitel

On the Use of Attention in Deep Learning Based Denoising Method for Ancient Cham Inscription Images

verfasst von : Tien-Nam Nguyen, Jean-Christophe Burie, Thi-Lan Le, Anne-Valerie Schweyer

Erschienen in: Document Analysis and Recognition – ICDAR 2021

Verlag: Springer International Publishing

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Abstract

Image denoising is one of the most important steps in the document image analysis pipeline thanks to its good effect into the rest of the workflow. However, the noise in historical documents is totally different from the common noise present in other classical problems of image processing. It is particularly the case of the image of Cham inscriptions obtained by the stamping of ancient stele. In this paper, we leverage the advantage of deep learning to adapt with these noisy conditions. The proposed network follows an encoder-decoder structure by combining convolution/deconvolution operators with symmetrical skip connections and residual blocks for improving reconstructed image. Furthermore, global attention fusion is proposed to learn the relevant regions in the image. Our experiments demonstrate the proposed method can’t only remove unwanted parts in the image, but also enhance the visual quality for the Cham inscriptions.

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Vorheriges Kapitel One-Model Ensemble-Learning for Text Recognition of Historical Printings
Nächstes Kapitel Visual FUDGE: Form Understanding via Dynamic Graph Editing
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Metadaten
Titel
On the Use of Attention in Deep Learning Based Denoising Method for Ancient Cham Inscription Images
verfasst von
Tien-Nam Nguyen
Jean-Christophe Burie
Thi-Lan Le
Anne-Valerie Schweyer
Copyright-Jahr
2021
Buch
Document Analysis and Recognition – ICDAR 2021

Print ISBN: 978-3-030-86548-1

Electronic ISBN: 978-3-030-86549-8

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-3-030-86549-8_26

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