An Auto-Encoder of Inscription Character Inpainting based on Branch Convolutional Channel Attention Module

An Auto-Encoder of Inscription Character Inpainting based on Branch Convolutional Channel Attention Module | 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 An Auto-Encoder of Inscription Character Inpainting based on Branch Convolutional Channel Attention Module Long Zhao, Zonglong Yuan, Yuhao Lou, Qingyu Xu, Xinxiao Qiao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3187267/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 Many inscriptions suffer severe damage as a result of artificial or natural causes, necessitating computer repair and protection. There isn’t a publicly accessible dataset of inscriptions, and the majority of character inpainting algorithms that are currently in use are directly derived from common image inpainting algorithms , which have insufficient feature extraction capabilities for the character of inscriptions and don’t have metrics for the weights of obscured and unobscured regions. We developed a Character Auto-Encoder (CAE) to improve the inpaint-ing capabilities of inscription characters and solve the aforementioned issues. The down-sampling module is replaced by the Branch Convolutional Channel Attention Module (BCCAM), which employs a branching structure to enhance the model’s capacity for representation and weights various areas of the character. First, we compared other inpainting models using the inscription dataset, and the CAE’s peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) increased by 0.82 and 0.016, respectively, compared to the baseline model. Next, we compared the Handwritten Chinese Character Inpainting Generative Adver-sarial Network (HCCI-GAN) with the handwritten Chinese character dataset, and the CAE’s design was relatively more ingenious and simpler. inscription character inpainting branch convolutional channel attention module U-Net character auto-encoder 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-3187267","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":220155965,"identity":"0c2063d6-259a-4dfb-8865-4c7e93b09545","order_by":0,"name":"Long Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYDACCSB+AMT8DMwNQIqZSC0JQCzZwEiqFoMDxGqRn9388EFCxR27zccT2yQYKqwTG9jPHsCrhXHOMWODhDPPkredeQjUciY9sYEnLwGvFmaJBDOJxLbDyWY3gLYwth1ObJDgMcCrhU0i/RtYi/EMkJZ/RGjhkcgB22JnIAHS0kCEFgmJnGKgXw4nSJx52GyRcCzduI0nB78W+RnpGx98qDhsz9+efPDGhxpr2X72M/i1wEBiAyh2EkC+I0o9ENhD1I+CUTAKRsEowAIAkR5GVivx+DIAAAAASUVORK5CYII=","orcid":"","institution":"Qilu University of Technology (Shandong Academy of Sciences)","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Long","middleName":"","lastName":"Zhao","suffix":""},{"id":220155966,"identity":"dc014bd6-673d-47a9-9a3d-f1ff5ae9a61e","order_by":1,"name":"Zonglong Yuan","email":"","orcid":"","institution":"Qilu University of Technology (Shandong Academy of Sciences)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zonglong","middleName":"","lastName":"Yuan","suffix":""},{"id":220155967,"identity":"f1355de4-0301-49c0-bed8-2b07d1c2531a","order_by":2,"name":"Yuhao Lou","email":"","orcid":"","institution":"Qilu University of Technology (Shandong Academy of Sciences)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yuhao","middleName":"","lastName":"Lou","suffix":""},{"id":220155968,"identity":"9b205d8c-a7e6-4eb2-8686-ed98b66257b4","order_by":3,"name":"Qingyu Xu","email":"","orcid":"","institution":"Qilu University of Technology (Shandong Academy of Sciences)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qingyu","middleName":"","lastName":"Xu","suffix":""},{"id":220155969,"identity":"ccb21ee3-15ca-4294-b5d0-1eda05398fb9","order_by":4,"name":"Xinxiao Qiao","email":"","orcid":"","institution":"Qilu University of Technology (Shandong Academy of Sciences)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xinxiao","middleName":"","lastName":"Qiao","suffix":""}],"badges":[],"createdAt":"2023-07-20 06:59:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3187267/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3187267/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":46008010,"identity":"db468f53-a02f-4c16-8673-15c67592fc7f","added_by":"auto","created_at":"2023-11-07 13:37:53","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3763569,"visible":true,"origin":"","legend":"","description":"","filename":"AnAutoEncoderofInscriptionCharacterInpaintingbasedonBranchConvolutionalChannelAttentionModule.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3187267/v1_covered_331a1126-ad4c-438a-9b85-624e1ba5c9e9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An Auto-Encoder of Inscription Character Inpainting based on Branch Convolutional Channel Attention Module","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":"inscription character inpainting, branch convolutional channel attention module, U-Net, character auto-encoder","lastPublishedDoi":"10.21203/rs.3.rs-3187267/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3187267/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Many inscriptions suffer severe damage as a result of artificial or natural causes, necessitating computer repair and protection. There isn’t a publicly accessible dataset of inscriptions, and the majority of character inpainting algorithms that are currently in use are directly derived from common image inpainting algorithms , which have insufficient feature extraction capabilities for the character of inscriptions and don’t have metrics for the weights of obscured and unobscured regions. We developed a Character Auto-Encoder (CAE) to improve the inpaint-ing capabilities of inscription characters and solve the aforementioned issues. The down-sampling module is replaced by the Branch Convolutional Channel Attention Module (BCCAM), which employs a branching structure to enhance the model’s capacity for representation and weights various areas of the character. First, we compared other inpainting models using the inscription dataset, and the CAE’s peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) increased by 0.82 and 0.016, respectively, compared to the baseline model. 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