Enhanced Threshold Segmentation for Accurate Digital Meter Reading Recognition using an Improved Northern Goshawk Optimization Algorithm

Enhanced Threshold Segmentation for Accurate Digital Meter Reading Recognition using an Improved Northern Goshawk Optimization Algorithm | 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 Enhanced Threshold Segmentation for Accurate Digital Meter Reading Recognition using an Improved Northern Goshawk Optimization Algorithm Chao Li, Baoqi Li, Hongli Liu, Changlong Lv, Yuzheng Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7506799/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract In the realm of industrial automation, digital meters are increasingly prevalent, yet manual meter reading remains inefficient and error-prone. This paper presents a novel computational method aimed at enhancing the precision and speed of digital meter reading recognition. The proposed approach commences with image preprocessing, involving grayscale conversion, histogram equalization to amplify meter digital information, and median filtering to eliminate noise. To tackle the challenge of image binarization in complex scenarios, we introduce an Improved Northern Goshawk Optimization (INGO) algorithm, which leverages Tent chaotic mapping and adaptive Cauchy variation to refine the OTSU threshold segmentation process. By expanding the search range and mitigating local optimal traps, INGO ensures the precise determination of the optimal binarization threshold. Here, we show that the proposed method achieves a substantial improvement in Peak Signal-to-Noise Ratio (PSNR), with an average increase of 6.35% compared to traditional OTSU and NGO-optimized methods, resulting in clearer and more accurate digital meter readings. This research underscores the potential of intelligent algorithms in automating industrial meter reading, contributing to enhanced efficiency and reliability in energy management systems. machine vision image processing meter reading recognition threshold segmentation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 25 Sep, 2025 Reviewers agreed at journal 05 Sep, 2025 Reviewers agreed at journal 05 Sep, 2025 Reviewers agreed at journal 05 Sep, 2025 Reviewers invited by journal 05 Sep, 2025 Editor assigned by journal 05 Sep, 2025 Submission checks completed at journal 05 Sep, 2025 First submitted to journal 01 Sep, 2025 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. 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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-7506799","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":511029133,"identity":"1cd99baa-ca95-44a1-9c80-adca8934fd96","order_by":0,"name":"Chao Li","email":"","orcid":"","institution":"Tianjin University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Li","suffix":""},{"id":511029134,"identity":"a783de87-22b1-4aa3-b634-76a5869fbf34","order_by":1,"name":"Baoqi Li","email":"","orcid":"","institution":"Tianjin University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Baoqi","middleName":"","lastName":"Li","suffix":""},{"id":511029135,"identity":"6a8f03be-2705-4472-8d5b-30b560c664a6","order_by":2,"name":"Hongli Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArUlEQVRIiWNgGAWjYBACPmYGNoYHFQzMII4EUVrYQFoSzhiQogWEEtsMGEjQws5j9iBx3h92gwPMB2/zMNjlEeEwHnODxG0GzAYH2JKteRiSi4nRYiYB0cJjJs3DcCCxgTgtc0Ba+L+RoqUBbAsbsVrYyiQSjhkzSx5mM7acY5BMWAs//+FtEh9q5JL5jjc/vPGmwo6wFhhIhsS/AbHqgcCOBLWjYBSMglEw0gAA85csk4ftYXMAAAAASUVORK5CYII=","orcid":"","institution":"Tianjin University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Hongli","middleName":"","lastName":"Liu","suffix":""},{"id":511029136,"identity":"c41bda43-156c-4b06-b402-0bc696646815","order_by":3,"name":"Changlong Lv","email":"","orcid":"","institution":"Tianjin University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Changlong","middleName":"","lastName":"Lv","suffix":""},{"id":511029137,"identity":"55826362-679b-4c5d-a310-9a1f814bd419","order_by":4,"name":"Yuzheng Liu","email":"","orcid":"","institution":"Tianjin University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yuzheng","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-09-01 09:38:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7506799/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7506799/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91403877,"identity":"ee5be1a2-bda0-4f6c-92c2-37c70bda721c","added_by":"auto","created_at":"2025-09-16 07:31:08","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1544723,"visible":true,"origin":"","legend":"","description":"","filename":"paper2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7506799/v1_covered_372b8fa4-8306-404b-859e-e66cae11dac3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhanced Threshold Segmentation for Accurate Digital Meter Reading Recognition using an Improved Northern Goshawk Optimization Algorithm","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"signal-image-and-video-processing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sivp","sideBox":"Learn more about [Signal, Image and Video Processing](http://link.springer.com/journal/11760)","snPcode":"11760","submissionUrl":"https://submission.nature.com/new-submission/11760/3","title":"Signal, Image and Video Processing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"machine vision, image processing, meter reading recognition, threshold segmentation","lastPublishedDoi":"10.21203/rs.3.rs-7506799/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7506799/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the realm of industrial automation, digital meters are increasingly prevalent, yet manual meter reading remains inefficient and error-prone. This paper presents a novel computational method aimed at enhancing the precision and speed of digital meter reading recognition. The proposed approach commences with image preprocessing, involving grayscale conversion, histogram equalization to amplify meter digital information, and median filtering to eliminate noise. To tackle the challenge of image binarization in complex scenarios, we introduce an Improved Northern Goshawk Optimization (INGO) algorithm, which leverages Tent chaotic mapping and adaptive Cauchy variation to refine the OTSU threshold segmentation process. By expanding the search range and mitigating local optimal traps, INGO ensures the precise determination of the optimal binarization threshold. Here, we show that the proposed method achieves a substantial improvement in Peak Signal-to-Noise Ratio (PSNR), with an average increase of 6.35% compared to traditional OTSU and NGO-optimized methods, resulting in clearer and more accurate digital meter readings. 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