Beyond Accuracy: A Critical Review of Document Image Binarization Evaluation Practices

Beyond Accuracy: A Critical Review of Document Image Binarization Evaluation Practices | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF survey Beyond Accuracy: A Critical Review of Document Image Binarization Evaluation Practices ET-Tahir Zemouri, Abdeljalil Gattal, Nabil Zerrouki, Fouzi Harrou, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8842898/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Document image binarization is a fundamental yet challenging task in document analysis, particularly for historical documents affected by complex degradations such as uneven illumination, bleed-through, stains, and ink fading. While substantial progress has been made, algorithms performance strongly depends on document characteristics and acquisition conditions. To support objective comparison, benchmarking initiatives such as the Document Image Binarization Competition (DIBCO) provide standardized datasets and pixel-level evaluation protocols. While existing studies largely focus on binarization accuracy, practical deployment additionally requires careful consideration of computational efficiency, as high-quality methods may incur prohibitive processing costs. This paper presents a critical review of document image binarization evaluation methodologies, with a particular focus on historical documents. We analyze the evolution of algorithms evaluated within the DIBCO frameworks, examine the strengths and limitations of commonly used performance metrics, and discuss the impact of blind evaluation protocols on algorithm ranking. Furthermore, we highlight the emerging importance of time-quality trade-offs and efficiency-aware evaluation criteria. The survey highlights the absence of a universally optimal binarization method and points to the need for adaptive, degradation-aware strategies and more comprehensive evaluation practices in future research. document image binarization historical documents evaluation metrics benchmarking DIBCO computational efficiency Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8842898","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"survey","associatedPublications":[],"authors":[{"id":592584931,"identity":"fa4f0e09-eddc-411d-845b-e6812bafc589","order_by":0,"name":"ET-Tahir Zemouri","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYBADHn4GBgMgzUyEUhgt2cBg2ECSFgaDA8Rqsec/fHTDx7Z7MsbHD29/8IPB2p4IW46l3ZzZVsxjdiatsLGHIZ2wNTyMPWa3edsSeMxu8Bg28DAcZiOshZn/G1iL8Qwew8Y/DId5CGth42EDazGQ4DFsBtoiQVjLGTazmzPOJfBIAP0yW8Yg3YCgFvb+w89ufChLsOdvP7zh45sKIkIMDRC2YxSMglEwCkYBMQAAfmM0tKy297QAAAAASUVORK5CYII=","orcid":"","institution":"Centre for development of advanced technologies (CDTA)","correspondingAuthor":true,"prefix":"","firstName":"ET-Tahir","middleName":"","lastName":"Zemouri","suffix":""},{"id":592584932,"identity":"51cbd0ba-f0f7-4aa2-8fa0-5b70128f9b03","order_by":1,"name":"Abdeljalil Gattal","email":"","orcid":"","institution":"Echahid Cheikh Larbi Tebessi University Laboratoire de Vision et d’Intelligence Artificielle (LAVIA)","correspondingAuthor":false,"prefix":"","firstName":"Abdeljalil","middleName":"","lastName":"Gattal","suffix":""},{"id":592584933,"identity":"22f81491-242a-49a5-8ca8-c4cb98f30a60","order_by":2,"name":"Nabil Zerrouki","email":"","orcid":"","institution":"Centre for development of advanced technologies (CDTA)","correspondingAuthor":false,"prefix":"","firstName":"Nabil","middleName":"","lastName":"Zerrouki","suffix":""},{"id":592584934,"identity":"61522da5-579f-46b9-88e4-06900c050fbb","order_by":3,"name":"Fouzi Harrou","email":"","orcid":"","institution":"King Abdullah University of Science and Technology (KAUST)","correspondingAuthor":false,"prefix":"","firstName":"Fouzi","middleName":"","lastName":"Harrou","suffix":""},{"id":592584935,"identity":"3df091dc-3bc0-43b4-9d14-69b0e0e55662","order_by":4,"name":"Ying Sun","email":"","orcid":"","institution":"King Abdullah University of Science and Technology (KAUST)","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Sun","suffix":""}],"badges":[],"createdAt":"2026-02-10 15:55:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8842898/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8842898/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102963962,"identity":"e3a5ee4a-5ec4-4a29-863f-577a6811c21e","added_by":"auto","created_at":"2026-02-19 04:21:00","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1264310,"visible":true,"origin":"","legend":"","description":"","filename":"ArticleIJDARAQuantitativeReviewofBinarizationAlgorithms.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8842898/v1_covered_77205ac3-f9df-4878-8cad-0414a148d3d5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Beyond Accuracy: A Critical Review of Document Image Binarization Evaluation Practices","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"document image binarization, historical documents, evaluation metrics, benchmarking, DIBCO, computational efficiency","lastPublishedDoi":"10.21203/rs.3.rs-8842898/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8842898/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDocument image binarization is a fundamental yet challenging task in document analysis, particularly for historical documents affected by complex degradations such as uneven illumination, bleed-through, stains, and ink fading. While substantial progress has been made, algorithms performance strongly depends on document characteristics and acquisition conditions. To support objective comparison, benchmarking initiatives such as the Document Image Binarization Competition (DIBCO) provide standardized datasets and pixel-level evaluation protocols. While existing studies largely focus on binarization accuracy, practical deployment additionally requires careful consideration of computational efficiency, as high-quality methods may incur prohibitive processing costs. This paper presents a critical review of document image binarization evaluation methodologies, with a particular focus on historical documents. We analyze the evolution of algorithms evaluated within the DIBCO frameworks, examine the strengths and limitations of commonly used performance metrics, and discuss the impact of blind evaluation protocols on algorithm ranking. 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