research-article

Binarization of historical document images using the local maximum and minimum

Authors:

Chew Lim TanAuthors Info & Claims

DAS '10: Proceedings of the 9th IAPR International Workshop on Document Analysis Systems

Pages 159 - 166

https://doi.org/10.1145/1815330.1815351

Published: 09 June 2010 Publication History

Abstract

This paper presents a new document image binarization technique that segments the text from badly degraded historical document images. The proposed technique makes use of the image contrast that is defined by the local image maximum and minimum. Compared with the image gradient, the image contrast evaluated by the local maximum and minimum has a nice property that it is more tolerant to the uneven illumination and other types of document degradation such as smear. Given a historical document image, the proposed technique first constructs a contrast image and then detects the high contrast image pixels which usually lie around the text stroke boundary. The document text is then segmented by using local thresholds that are estimated from the detected high contrast pixels within a local neighborhood window. The proposed technique has been tested over the dataset that is used in the recent Document Image Binarization Contest (DIBCO) 2009. Experiments show its superior performance.

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Cited By

Nafchi ACheriet M(2025)BA-GAN: A Boundary-Aware Generative Adversarial Network for Document RestorationIntelligent Systems and Pattern Recognition10.1007/978-3-031-82153-0_26(344-358)Online publication date: 5-Mar-2025
https://doi.org/10.1007/978-3-031-82153-0_26
Yang ZZuo SZhou YHe JShi J(2024)A Review of Document Binarization: Main Techniques, New Challenges, and TrendsElectronics10.3390/electronics1307139413:7(1394)Online publication date: 7-Apr-2024
https://doi.org/10.3390/electronics13071394
Jayanthi JMaheswari P(2024)Comparative study: enhancing legibility of ancient Indian script images from diverse stone background structures using 34 different pre-processing methodsHeritage Science10.1186/s40494-024-01169-612:1Online publication date: 20-Feb-2024
https://doi.org/10.1186/s40494-024-01169-6
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Index Terms

Binarization of historical document images using the local maximum and minimum
1. Applied computing
  1. Document management and text processing
    1. Document capture
      1. Document analysis
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Interest point and salient region detections

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cover image ACM Other conferences

DAS '10: Proceedings of the 9th IAPR International Workshop on Document Analysis Systems

June 2010

490 pages

ISBN:9781605587738

DOI:10.1145/1815330

General Chairs:
David Doermann
University of Maryland, College Park
,
Venu Govindaraju
University at Buffalo, SUNY
,
Daniel Lopresti
Lehigh University
,
Prem Natarajan
Raytheon BBN Technologies

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 09 June 2010

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DAS '10

DAS '10: The Eighth IAPR International Workshop on Document Analysis Systems

June 9 - 11, 2010

Massachusetts, Boston, USA

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Cited By

Nafchi ACheriet M(2025)BA-GAN: A Boundary-Aware Generative Adversarial Network for Document RestorationIntelligent Systems and Pattern Recognition10.1007/978-3-031-82153-0_26(344-358)Online publication date: 5-Mar-2025
https://doi.org/10.1007/978-3-031-82153-0_26
Yang ZZuo SZhou YHe JShi J(2024)A Review of Document Binarization: Main Techniques, New Challenges, and TrendsElectronics10.3390/electronics1307139413:7(1394)Online publication date: 7-Apr-2024
https://doi.org/10.3390/electronics13071394
Jayanthi JMaheswari P(2024)Comparative study: enhancing legibility of ancient Indian script images from diverse stone background structures using 34 different pre-processing methodsHeritage Science10.1186/s40494-024-01169-612:1Online publication date: 20-Feb-2024
https://doi.org/10.1186/s40494-024-01169-6
Munivel MEnigo V(2024)Performance of Binarization Algorithms on Tamizhi Inscription Images: An AnalysisACM Transactions on Asian and Low-Resource Language Information Processing10.1145/365658323:5(1-29)Online publication date: 10-May-2024
https://dl.acm.org/doi/10.1145/3656583
Poudel URegmi AStamenkovic ZRaja S(2024)Applicability of OCR Engines for Text Recognition in Vehicle Number Plates, Receipts and HandwritingJournal of Circuits, Systems and Computers10.1142/S021812662350321832:18Online publication date: 2-Feb-2024
https://doi.org/10.1142/S0218126623503218
Panov SPanova S(2024)Binarization via Local Gradient-like Differences in four Directions2024 International Conference Automatics and Informatics (ICAI)10.1109/ICAI63388.2024.10851623(414-419)Online publication date: 10-Oct-2024
https://doi.org/10.1109/ICAI63388.2024.10851623
Xie XLi YLiu YZhang ZWang ZXiong WBai X(2024)WAS: Dataset and Methods for Artistic Text SegmentationComputer Vision – ECCV 202410.1007/978-3-031-73001-6_14(237-254)Online publication date: 27-Nov-2024
https://doi.org/10.1007/978-3-031-73001-6_14
Yu HFu TLi BXue X(2024)EAFormer: Scene Text Segmentation with Edge-Aware TransformersComputer Vision – ECCV 202410.1007/978-3-031-72698-9_24(410-427)Online publication date: 26-Oct-2024
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Peer MKleber FSablatnig R(2024)SAGHOG: Self-supervised Autoencoder for Generating HOG Features for Writer RetrievalDocument Analysis and Recognition - ICDAR 202410.1007/978-3-031-70536-6_8(121-138)Online publication date: 3-Sep-2024
https://doi.org/10.1007/978-3-031-70536-6_8
Biswas RSarkhel SRoy SPal U(2023)TransDocUNet: A Transformer-based UNet Architecture for Degraded Document Image BinarizationProceedings of the Fourteenth Indian Conference on Computer Vision, Graphics and Image Processing10.1145/3627631.3627639(1-9)Online publication date: 15-Dec-2023
https://dl.acm.org/doi/10.1145/3627631.3627639
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