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2020 | OriginalPaper | Buchkapitel

Dewarping Document Image by Displacement Flow Estimation with Fully Convolutional Network

verfasst von : Guo-Wang Xie, Fei Yin, Xu-Yao Zhang, Cheng-Lin Liu

Erschienen in: Document Analysis Systems

Verlag: Springer International Publishing

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Abstract

As camera-based documents are increasingly used, the rectification of distorted document images becomes a need to improve the recognition performance. In this paper, we propose a novel framework for both rectifying distorted document image and removing background finely, by estimating pixel-wise displacements using a fully convolutional network (FCN). The document image is rectified by transformation according to the displacements of pixels. The FCN is trained by regressing displacements of synthesized distorted documents, and to control the smoothness of displacements, we propose a Local Smooth Constraint (LSC) in regularization. Our approach is easy to implement and consumes moderate computing resource. Experiments proved that our approach can dewarp document images effectively under various geometric distortions, and has achieved the state-of-the-art performance in terms of local details and overall effect.

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Vorheriges Kapitel ACMU-Nets: Attention Cascading Modular U-Nets Incorporating Squeeze and Excitation Blocks

Nächstes Kapitel Building Super-Resolution Image Generator for OCR Accuracy Improvement

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Titel: Dewarping Document Image by Displacement Flow Estimation with Fully Convolutional Network
verfasst von: Guo-Wang Xie
Fei Yin
Xu-Yao Zhang
Cheng-Lin Liu
Verlag: Springer International Publishing
Buch: Document Analysis Systems
Print ISBN: 978-3-030-57057-6

Electronic ISBN: 978-3-030-57058-3

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-57058-3_10

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