Information Extraction from Multi-Layout Invoice Images using FATURA Dataset

Information Extraction from Multi-Layout Invoice Images using FATURA Dataset | 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 Research Article Information Extraction from Multi-Layout Invoice Images using FATURA Dataset Mahmoud Limam, Marwa Dhiaf, Yousri Kessentini This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3711463/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 analysis and understanding models often require extensive annotated data to be trained. However, various document-related tasks extend beyond mere text transcription, requiring both textual content and precise bounding-box annotations to identify different document elements. Collecting such data becomes particularly challenging, especially in the context of invoices, where privacy concerns add an additional layer of complexity. In this paper, we introduce FATURA, a pivotal resource for researchers in the field of document analysis and understanding. FATURA is a highly diverse dataset featuring multi-layout, annotated invoice document images. Comprising $10,000$ invoices with $50$ distinct layouts, it represents the largest openly accessible image dataset of invoice documents known to date. We also provide an extensive evaluation of different information extraction methods under diverse training and evaluation scenarios, including a visual-based approach using object detection for text region classification, a multi-modal strategy integrating visual and textual data for granular content comprehension, and finally, a hybrid approach combining these methods. The dataset is freely accessible at this \href{ https://zenodo.org/record/8261508}{URL\footnote{https://zenodo.org/record/8261508}} , empowering researchers to advance the field of document analysis and understanding. Open invoice images dataset Document layout analysis Key-value extraction Document Layout analysis 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-3711463","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":257048898,"identity":"6e127d11-519b-42cf-b9fd-a0d6fc60540b","order_by":0,"name":"Mahmoud Limam","email":"","orcid":"","institution":"Digital Research Centre of Sfax","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mahmoud","middleName":"","lastName":"Limam","suffix":""},{"id":257048899,"identity":"5ce478a4-9377-4343-890c-6857c1d2dfe9","order_by":1,"name":"Marwa Dhiaf","email":"data:image/png;base64,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","orcid":"","institution":"Digital Research Centre of Sfax","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Marwa","middleName":"","lastName":"Dhiaf","suffix":""},{"id":257048900,"identity":"1d896d68-9873-4f31-a2dc-0873376e3dea","order_by":2,"name":"Yousri Kessentini","email":"","orcid":"","institution":"Digital Research Centre of Sfax","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yousri","middleName":"","lastName":"Kessentini","suffix":""}],"badges":[],"createdAt":"2023-12-05 18:59:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3711463/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3711463/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49054663,"identity":"3e5b4548-a0fa-40e0-ac28-a7a16f39d236","added_by":"auto","created_at":"2024-01-02 11:37:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":887823,"visible":true,"origin":"","legend":"","description":"","filename":"IJMIR.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3711463/v1_covered_8a4a23bb-035b-4628-9b9f-80875f0cdae7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Information Extraction from Multi-Layout Invoice Images using FATURA Dataset ","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Open invoice images dataset ,Document layout analysis, Key-value extraction, Document Layout analysis","lastPublishedDoi":"10.21203/rs.3.rs-3711463/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3711463/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Document analysis and understanding models often require extensive annotated data to be trained. However, various document-related tasks extend beyond mere text transcription, requiring both textual content and precise bounding-box annotations to identify different document elements. Collecting such data becomes particularly challenging, especially in the context of invoices, where privacy concerns add an additional layer of complexity.\nIn this paper, we introduce FATURA, a pivotal resource for researchers in the field of document analysis and understanding. FATURA is a highly diverse dataset featuring multi-layout, annotated invoice document images. Comprising $10,000$ invoices with $50$ distinct layouts, it represents the largest openly accessible image dataset of invoice documents known to date. We also provide an extensive evaluation of different information extraction methods under diverse training and evaluation scenarios, including a visual-based approach using object detection for text region classification, a multi-modal strategy integrating visual and textual data for granular content comprehension, and finally, a hybrid approach combining these methods. The dataset is freely accessible at this \\href{https://zenodo.org/record/8261508}{URL\\footnote{https://zenodo.org/record/8261508}}, empowering researchers to advance the field of document analysis and understanding. 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