PiTeQA-Net: Picto-Textual Question Answering Network for Multimodal Document Image Analysis

PiTeQA-Net: Picto-Textual Question Answering Network for Multimodal Document Image Analysis | 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 PiTeQA-Net: Picto-Textual Question Answering Network for Multimodal Document Image Analysis Li Jiaming This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4007496/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 By integrating visual and natural language understanding, Visual Question Answering (VQA) holds promise for enhancing the intelligence of computer systems, thereby improving user work efficiency. However, current research in the VQA field faces two major challenges: low precision in feature pre-extraction and low efficiency in feature fusion. This study proposes a pre-trained framework based on RSFE-TBT for question-answering tasks aimed at improving feature pre-extraction accuracy and feature fusion efficiency across different modalities. Addressing the issue of low pre-extraction accuracy in existing methods, ResNet50-SF is proposed for pre-extracting image features. Considering ResNet50's limitations in recognizing small objects and specific spatial positions, a bidirectional feature pyramid network(BiFPN) with spatial attention is introduced. Text block features bridging image and text are introduced to address challenges in aligning multi-modal features and fusion efficiency. These encompass block position, shape, sequence, and relative arrangements. Image features are segmented into semantic and spatial aspects, while text blocks are divided into positional and index attributes. Efficient multimodal fusion is achieved with multi-layer Transformer Encoders. Experimental results on the MultiDoc-InfoExtract Dataset demonstrate superior performance of this method in Semantic Entity Recognition (SER) and Relation Extraction (RE) tasks, achieving F1, Precision, and Recall scores of 0.975, 0.975, and 0.975, respectively, in SER tasks, and 0.969, 0.953, and 0.986, respectively, in RE tasks, with a single image inference time of only 0.082s. Ablation studies validate the significance of the improved image feature extraction model, the inclusion of text block features, feature refinement strategies, and the Transformer Encoder architecture in enhancing the performance of the question-answering system. Additionally, comparative studies have shown that RSFE-TBT outperforms competing models regarding accuracy, speed, and size. Multi-modal Question-Answering System Semantic Entity Recognition Relation Extraction 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-4007496","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276395176,"identity":"b5d5f9a6-6da1-4050-85fa-2d4ee6c017af","order_by":0,"name":"Li Jiaming","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYDACCQbGAxIMNgkQHhtxWhiAWtKAWphJ0cLAcJgELebS7Q8OWOaczzO43X+A4UPZYQb+2Q34tVjOOZBwQHLb7WKDO4cZGGecO8wgcecAfi0GNxIOgLQkbriRzMDM23aYwUAigZCWxAaglnMQLX+J05LMANRyAKKFkRgtlnOOgbQkF0veOWxwsOdcOo/EDQJagCH28LHkNrs8vtuNDx/8KLOW459ByGFAzCwBYkEiiIEHv3qoFsYPUC2jYBSMglEwCrACAFnOSnr/CjviAAAAAElFTkSuQmCC","orcid":"","institution":"Beijing University of Posts and Telecommunications","correspondingAuthor":true,"prefix":"","firstName":"Li","middleName":"","lastName":"Jiaming","suffix":""}],"badges":[],"createdAt":"2024-03-03 04:14:12","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4007496/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4007496/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56526022,"identity":"69eb4340-b9ea-4b86-9b26-ac952027a5cf","added_by":"auto","created_at":"2024-05-15 10:14:36","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1136884,"visible":true,"origin":"","legend":"","description":"","filename":"PiTeQANet.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4007496/v1_covered_cb51b827-3997-49b6-83de-a441a72602b7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"PiTeQA-Net: Picto-Textual Question Answering Network for Multimodal Document Image Analysis","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":true,"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":"Multi-modal, Question-Answering System, Semantic Entity Recognition, Relation Extraction","lastPublishedDoi":"10.21203/rs.3.rs-4007496/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4007496/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"By integrating visual and natural language understanding, Visual Question Answering (VQA) holds promise for enhancing the intelligence of computer systems, thereby improving user work efficiency. 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