loading
Documents

Research.Publish.Connect.

Paper

Authors: Vijaya Ramanna 1 ; Saqib Bukhari 2 and Andreas Dengel 3

Affiliations: 1 Informatik, Technical University of Kaiserlautern, Kaiserslautern, Germany ; 2 Department of Knowledge Management, Deutsches Forschungszentrum für Künstliche Intelligenz GmbH (DFKI), Kaiserlsutern, Germany ; 3 Informatik, Technical University of Kaiserlautern, Kaiserslautern, Germany, Department of Knowledge Management, Deutsches Forschungszentrum für Künstliche Intelligenz GmbH (DFKI), Kaiserlsutern, Germany

ISBN: 978-989-758-351-3

Keyword(s): Document Image Dewarping, Deep Learning, Geometric Distortion, Page Curl, Line Curl.

Abstract: The distorted images have been a major problem for Optical Character Recognition (OCR). In order to perform OCR on distorted images, dewarping has become a principal preprocessing step. This paper presents a new document dewarping method that removes curl and geometric distortion of modern and historical documents. Finally, the proposed method is evaluated and compared to the existing Computer Vision based method. Most of the traditional dewarping algorithms are created based on the text line feature extraction and segmentation. However, textual content extraction and segmentation can be sophisticated. Hence, the new technique is proposed, which doesn’t need any complicated methods to process the text lines. The proposed method is based on Deep Learning and it can be applied on all type of text documents and also documents with images and graphics. Moreover, there is no preprocessing required to apply this method on warped images. In the proposed system, the document distortion proble m is treated as an image-to-image translation. The new method is implemented using a very powerful pix2pixhd network by utilizing Conditional Generative Adversarial Networks (CGAN). The network is trained on UW3 dataset by supplying distorted document as an input and cleaned image as the target. The generated images from the proposed method are cleanly dewarped and they are of high-resolution. Furthermore, these images can be used to perform OCR. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.227.254.12

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Bajjer Ramanna, V.; Bukhari, S. and Dengel, A. (2019). Document Image Dewarping using Deep Learning.In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-351-3, pages 524-531. DOI: 10.5220/0007368405240531

@conference{icpram19,
author={Vijaya Kumar Bajjer Ramanna. and Saqib Bukhari. and Andreas Dengel.},
title={Document Image Dewarping using Deep Learning},
booktitle={Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2019},
pages={524-531},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007368405240531},
isbn={978-989-758-351-3},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Document Image Dewarping using Deep Learning
SN - 978-989-758-351-3
AU - Bajjer Ramanna, V.
AU - Bukhari, S.
AU - Dengel, A.
PY - 2019
SP - 524
EP - 531
DO - 10.5220/0007368405240531

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.