[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-191575-105":3,"detail-sidebar-cat-1-en-105":81,"doc-detail-191575-en":126},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","document-metadata-extraction-multimodal","Document Metadata Extraction (Multimodal)","","This document outlines a process for extracting metadata from documents using a multimodal approach involving image analysis and OCR. The system takes raw document input and compares it against standard templates to detect potential fraud. If a high similarity is found, the document is classified, and its text is extracted using OCR. This extracted text is then verified against a database of real customer details. Based on the verification, the system outputs either \"Real Document\" if the details match, or \"Error in Data: Potential Fraud\" if there are discrepancies or the data is not found, indicating a possible fraudulent document.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/template/","Template",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/template/general/","General",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/template/document-metadata-extraction-multimodal/191575/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/document-metadata-extraction-multimodal/191575.png","ImageObject",442,249,{"name":42,"@type":43},"Clementine","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-09-27","2026-09-03",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",5,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"How does the system identify potentially fraudulent documents?","Question",{"text":63,"@type":64},"The system first compares the input document against standard templates. If a high similarity match is found, it proceeds to verify the extracted text against a database of real customer details. Discrepancies or lack of found data can flag a document as potentially fraudulent.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"What is the role of OCR in this system?",{"text":68,"@type":64},"OCR (Optical Character Recognition) is used to extract text from the document image. This extracted text is crucial for the subsequent verification step against the database of real customer details.",{"name":70,"@type":61,"acceptedAnswer":71},"What are the possible outputs of the document processing system?",{"text":72,"@type":64},"The system can output \"Real Document\" if all details are verified successfully, \"Fraud Document\" if the initial template matching indicates low similarity, or \"Error in Data: Potential Fraud\" if there are verification issues with the extracted text.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},191575,1789979786,{"code":4,"msg":82,"data":83},"success",[84,89,94,99,104,109,114,119,123],{"id":85,"doc_module":22,"doc_module_name":25,"category_name":86,"show_sort_weight":87,"slug":88},11,"Presentations",90,"presentations",{"id":90,"doc_module":22,"doc_module_name":25,"category_name":91,"show_sort_weight":92,"slug":93},12,"Resumes",80,"resumes",{"id":95,"doc_module":22,"doc_module_name":25,"category_name":96,"show_sort_weight":97,"slug":98},14,"Invoices",70,"invoices",{"id":100,"doc_module":22,"doc_module_name":25,"category_name":101,"show_sort_weight":102,"slug":103},15,"Posters",60,"posters",{"id":105,"doc_module":22,"doc_module_name":25,"category_name":106,"show_sort_weight":107,"slug":108},16,"Social Media",50,"social-media",{"id":110,"doc_module":22,"doc_module_name":25,"category_name":111,"show_sort_weight":112,"slug":113},17,"Forms",40,"forms",{"id":115,"doc_module":22,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},18,"Letters",30,"letters",{"id":120,"doc_module":22,"doc_module_name":25,"category_name":121,"show_sort_weight":55,"slug":122},21,"Paper Templates","papers-templates",{"id":124,"doc_module":22,"doc_module_name":25,"category_name":29,"show_sort_weight":4,"slug":125},158,"general-158",{"code":4,"msg":82,"data":127},{"doc_id":79,"user_id":128,"nickname":42,"user_avatar":129,"doc_module":22,"category_id":124,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":11,"file_id":130,"file_url":131,"file_type":132,"file_size":133,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":134,"language":135,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":136,"faqs":137,"seo_title":138,"seo_description":12,"update_tm":139,"read_time":30},1374391974564,"https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002","cbCailwxGtxjiJDi","https://ap.wps.com/l/cbCailwxGtxjiJDi","pdf",536246,8,"English","# Document Metadata Extraction (Multimodal)\n## Instructions\n## Language Rules\n## Field Specifications\n### Title Rules\n### `title_en`\n### `language`\n### abstract\n### `keywords`\n### `toc`\n### `faqs`\n### `category`\n## Output Requirements (STRICT)\n## Inputs","[{\"question\":\"How does the system identify potentially fraudulent documents?\",\"answer\":\"The system first compares the input document against standard templates. If a high similarity match is found, it proceeds to verify the extracted text against a database of real customer details. Discrepancies or lack of found data can flag a document as potentially fraudulent.\"},{\"question\":\"What is the role of OCR in this system?\",\"answer\":\"OCR (Optical Character Recognition) is used to extract text from the document image. This extracted text is crucial for the subsequent verification step against the database of real customer details.\"},{\"question\":\"What are the possible outputs of the document processing system?\",\"answer\":\"The system can output \\\"Real Document\\\" if all details are verified successfully, \\\"Fraud Document\\\" if the initial template matching indicates low similarity, or \\\"Error in Data: Potential Fraud\\\" if there are verification issues with the extracted text.\"}]","Document Metadata Extraction (Multimodal) | PDF",1788409443]