Two-stage generative adversarial networks for binarization of color document images
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Suh, Sungho | - |
dc.contributor.author | Kim, Jihun | - |
dc.contributor.author | Lukowicz, Paul | - |
dc.contributor.author | Lee, Yong Oh | - |
dc.date.accessioned | 2024-04-16T02:32:19Z | - |
dc.date.available | 2024-04-16T02:32:19Z | - |
dc.date.issued | 2022-10-01 | - |
dc.identifier.issn | 0031-3203 | - |
dc.identifier.issn | 1873-5142 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/32965 | - |
dc.description.abstract | Document image enhancement and binarization methods are often used to improve the accuracy and efficiency of document image analysis tasks such as text recognition. Traditional non-machine-learning methods are constructed on low-level features in an unsupervised manner but have difficulty with binarization on documents with severely degraded backgrounds. Convolutional neural network (CNN)based methods focus only on grayscale images and on local textual features. In this paper, we propose a two stage color document image enhancement and binarization method using generative adversarial neural networks. In the first stage, four color-independent adversarial networks are trained to extract color foreground information from an input image for document image enhancement. In the second stage, two independent adversarial networks with global and local features are trained for image binarization of documents of variable size. For the adversarial neural networks, we formulate loss functions between a discriminator and generators having an encoder-decoder structure. Experimental results show that the proposed method achieves better performance than many classical and state-of-the-art algorithms over the Document Image Binarization Contest (DIBCO) datasets, the LRDE Document Binarization Dataset (LRDE DBD), and our shipping label image dataset. We plan to release the shipping label dataset as well as our implementation code at github.com/opensuh/DocumentBinarization/. (c) 2022 Published by Elsevier Ltd. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | ELSEVIER SCI LTD | - |
dc.title | Two-stage generative adversarial networks for binarization of color document images | - |
dc.type | Article | - |
dc.publisher.location | 영국 | - |
dc.identifier.doi | 10.1016/j.patcog.2022.108810 | - |
dc.identifier.scopusid | 2-s2.0-85131101041 | - |
dc.identifier.wosid | 000808339300003 | - |
dc.identifier.bibliographicCitation | PATTERN RECOGNITION, v.130 | - |
dc.citation.title | PATTERN RECOGNITION | - |
dc.citation.volume | 130 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.subject.keywordPlus | ENHANCEMENT | - |
dc.subject.keywordAuthor | Document image binarization | - |
dc.subject.keywordAuthor | Generative adversarial networks | - |
dc.subject.keywordAuthor | Optical character recognition | - |
dc.subject.keywordAuthor | Color document image enhancement | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
94, Wausan-ro, Mapo-gu, Seoul, 04066, Korea02-320-1314
COPYRIGHT 2020 HONGIK UNIVERSITY. ALL RIGHTS RESERVED.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.