research-article

Assessing Binarization Techniques for Document Images

Authors:

Rafael Dueire Lins,

Marcos Martins de Almeida,

Rodrigo Barros Bernardino,

Darlisson Jesus,

José Mário OliveiraAuthors Info & Claims

DocEng '17: Proceedings of the 2017 ACM Symposium on Document Engineering

Pages 183 - 192

https://doi.org/10.1145/3103010.3103021

Published: 31 August 2017 Publication History

Abstract

Image binarization is a technique widely used for documents as monochromatic documents claim for far less space for storage and computer bandwidth for network transmission than their color or even grayscale equivalent. Paper color, texture, aging, translucidity, kind and color of ink used in handwritting, printing process, digitalization process, etc., are some of the factors that affect binarization. No algorithm is good enough to be a winner in the binarization of all kinds of documents. This paper presents a methodology to assess the performance of binarization algorithms for a wide variety of text documents, allowing a judicious quantitative choice of the best algorithms and their parameters.

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Cited By

Nazeer HDamodaran N(2023)FPGA-based point processing for denoising of the show-through effect in scanned document imagesJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2022.12.00135:1(296-309)Online publication date: Jan-2023
https://doi.org/10.1016/j.jksuci.2022.12.001
Bhowmik SBhowmik S(2023)Document Image BinarizationDocument Layout Analysis10.1007/978-981-99-4277-0_2(11-30)Online publication date: 1-Aug-2023
https://doi.org/10.1007/978-981-99-4277-0_2
Michalak HOkarma K(2023)Adaptive Binarization of Metal Nameplate Images Using the Pixel Voting ApproachComputer Vision and Graphics10.1007/978-3-031-22025-8_10(137-149)Online publication date: 11-Feb-2023
https://doi.org/10.1007/978-3-031-22025-8_10
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Index Terms

Assessing Binarization Techniques for Document Images
1. Applied computing
  1. Computers in other domains
    1. Publishing

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Information & Contributors

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Published In

cover image ACM Conferences

DocEng '17: Proceedings of the 2017 ACM Symposium on Document Engineering

August 2017

242 pages

ISBN:9781450346894

DOI:10.1145/3103010

General Chair:
Kenneth Camilleri
University of Malta, Malta
,
Program Chair:
Alexandra Bonnici
University of Malta, Malta

Copyright © 2017 ACM.

© 2017 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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SIGDOC: ACM Special Interest Group on Systems Documentation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 August 2017

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CNPq - Brazilian Government

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DocEng '17

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SIGWEB

DocEng '17: ACM Symposium on Document Engineering 2017

September 4 - 7, 2017

Valletta, Malta

Acceptance Rates

DocEng '17 Paper Acceptance Rate 13 of 71 submissions, 18%;

Overall Acceptance Rate 194 of 564 submissions, 34%

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Cited By

Nazeer HDamodaran N(2023)FPGA-based point processing for denoising of the show-through effect in scanned document imagesJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2022.12.00135:1(296-309)Online publication date: Jan-2023
https://doi.org/10.1016/j.jksuci.2022.12.001
Bhowmik SBhowmik S(2023)Document Image BinarizationDocument Layout Analysis10.1007/978-981-99-4277-0_2(11-30)Online publication date: 1-Aug-2023
https://doi.org/10.1007/978-981-99-4277-0_2
Michalak HOkarma K(2023)Adaptive Binarization of Metal Nameplate Images Using the Pixel Voting ApproachComputer Vision and Graphics10.1007/978-3-031-22025-8_10(137-149)Online publication date: 11-Feb-2023
https://doi.org/10.1007/978-3-031-22025-8_10
Lins RBernardino RBarboza ROliveira R(2022)The Winner Takes It All: Choosing the “best” Binarization Algorithm for Photographed DocumentsDocument Analysis Systems10.1007/978-3-031-06555-2_4(48-64)Online publication date: 18-May-2022
https://doi.org/10.1007/978-3-031-06555-2_4
Arnia FSaddami KMunadi K(2021)DCNet: Noise-Robust Convolutional Neural Networks for Degradation Classification on Ancient DocumentsJournal of Imaging10.3390/jimaging70701147:7(114)Online publication date: 12-Jul-2021
https://doi.org/10.3390/jimaging7070114
Atanasiu VMarthot-Santaniello I(2021)Personalizing image enhancement for critical visual tasks: improved legibility of papyri using color processing and visual illusionsInternational Journal on Document Analysis and Recognition (IJDAR)10.1007/s10032-021-00386-025:2(129-160)Online publication date: 27-Dec-2021
https://doi.org/10.1007/s10032-021-00386-0
Habibunnisha NNedumaran D(2021)Hardware-Based Document Image Thresholding Techniques Using DSP Builder and SimulinkArtificial Intelligence and Evolutionary Computations in Engineering Systems10.1007/978-981-16-2674-6_16(207-220)Online publication date: 19-Aug-2021
https://doi.org/10.1007/978-981-16-2674-6_16
Lins RBernardino RSmith EKavallieratou E(2021)ICDAR 2021 Competition on Time-Quality Document Image BinarizationDocument Analysis and Recognition – ICDAR 202110.1007/978-3-030-86337-1_47(708-722)Online publication date: 2-Sep-2021
https://doi.org/10.1007/978-3-030-86337-1_47
Omar IHaboubi SBenzarti F(2020)Toward a Binarization Framework resolving the Maghrebian Font Database challenges2020 17th International Multi-Conference on Systems, Signals & Devices (SSD)10.1109/SSD49366.2020.9364086(462-466)Online publication date: 20-Jul-2020
https://doi.org/10.1109/SSD49366.2020.9364086
Tensmeyer CMartinez T(2020)Historical Document Image Binarization: A ReviewSN Computer Science10.1007/s42979-020-00176-11:3Online publication date: 16-May-2020
https://doi.org/10.1007/s42979-020-00176-1
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