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Adaptive dewarping of severely warped camera-captured document images based on document map generation

Automated dewarping of camera-captured handwritten documents is a challenging research problem in Computer Vision and Pattern Recognition. Most available systems assume the shape of the camera-captured image boundaries to be anywhere between trapezoidal and octahedral, with linear distortion in area...

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Autores principales: Nachappa, C. H., Rani, N. Shobha, Pati, Peeta Basa, Gokulnath, M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9838515/
https://www.ncbi.nlm.nih.gov/pubmed/36687334
http://dx.doi.org/10.1007/s10032-022-00425-4
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author Nachappa, C. H.
Rani, N. Shobha
Pati, Peeta Basa
Gokulnath, M.
author_facet Nachappa, C. H.
Rani, N. Shobha
Pati, Peeta Basa
Gokulnath, M.
author_sort Nachappa, C. H.
collection PubMed
description Automated dewarping of camera-captured handwritten documents is a challenging research problem in Computer Vision and Pattern Recognition. Most available systems assume the shape of the camera-captured image boundaries to be anywhere between trapezoidal and octahedral, with linear distortion in areas between the boundaries for dewarping. The majority of the state-of-the-art applications successfully dewarp the simple-to-medium range geometrical distortions with partial selection of control points by a user. The proposed work implements a fully automated technique for control point detection from simple-to-complex geometrical distortions in camera-captured document images. The input image is subject to preprocessing, corner point detection, document map generation, and rendering of the de-warped document image. The proposed algorithm has been tested on five different camera-captured document datasets (one internal and four external publicly available) consisting of 958 images. Both quantitative and qualitative evaluations have been performed to test the efficacy of the proposed system. On the quantitative front, an Intersection Over Union (IoU) score of 0.92, 0.88, and 0.80 for document map generation for low-, medium-, and high-complexity datasets, respectively. Additionally, accuracies of the recognized texts, obtained from a market leading OCR engine, are utilized for quantitative comparative analysis on document images before and after the proposed enhancement. Finally, the qualitative analysis visually establishes the system’s reliability by demonstrating improved readability even for severely distorted image samples.
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spelling pubmed-98385152023-01-17 Adaptive dewarping of severely warped camera-captured document images based on document map generation Nachappa, C. H. Rani, N. Shobha Pati, Peeta Basa Gokulnath, M. Int J Doc Anal Recognit Original Paper Automated dewarping of camera-captured handwritten documents is a challenging research problem in Computer Vision and Pattern Recognition. Most available systems assume the shape of the camera-captured image boundaries to be anywhere between trapezoidal and octahedral, with linear distortion in areas between the boundaries for dewarping. The majority of the state-of-the-art applications successfully dewarp the simple-to-medium range geometrical distortions with partial selection of control points by a user. The proposed work implements a fully automated technique for control point detection from simple-to-complex geometrical distortions in camera-captured document images. The input image is subject to preprocessing, corner point detection, document map generation, and rendering of the de-warped document image. The proposed algorithm has been tested on five different camera-captured document datasets (one internal and four external publicly available) consisting of 958 images. Both quantitative and qualitative evaluations have been performed to test the efficacy of the proposed system. On the quantitative front, an Intersection Over Union (IoU) score of 0.92, 0.88, and 0.80 for document map generation for low-, medium-, and high-complexity datasets, respectively. Additionally, accuracies of the recognized texts, obtained from a market leading OCR engine, are utilized for quantitative comparative analysis on document images before and after the proposed enhancement. Finally, the qualitative analysis visually establishes the system’s reliability by demonstrating improved readability even for severely distorted image samples. Springer Berlin Heidelberg 2023-01-09 2023 /pmc/articles/PMC9838515/ /pubmed/36687334 http://dx.doi.org/10.1007/s10032-022-00425-4 Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Original Paper
Nachappa, C. H.
Rani, N. Shobha
Pati, Peeta Basa
Gokulnath, M.
Adaptive dewarping of severely warped camera-captured document images based on document map generation
title Adaptive dewarping of severely warped camera-captured document images based on document map generation
title_full Adaptive dewarping of severely warped camera-captured document images based on document map generation
title_fullStr Adaptive dewarping of severely warped camera-captured document images based on document map generation
title_full_unstemmed Adaptive dewarping of severely warped camera-captured document images based on document map generation
title_short Adaptive dewarping of severely warped camera-captured document images based on document map generation
title_sort adaptive dewarping of severely warped camera-captured document images based on document map generation
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9838515/
https://www.ncbi.nlm.nih.gov/pubmed/36687334
http://dx.doi.org/10.1007/s10032-022-00425-4
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