An Intelligent Handwritten Malayalam Text Recognition Using Optimized Machine Learning Based Optimization Frameworks

An Intelligent Handwritten Malayalam Text Recognition Using Optimized Machine Learning Based Optimization Frameworks | 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 Intelligent Handwritten Malayalam Text Recognition Using Optimized Machine Learning Based Optimization Frameworks Gargy G, A Shajin Nargunam This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9079840/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 Traditional Machine Learning (ML) and Deep Learning (DL) models are failed to predict the characters, overlapping sentences and diacritical marks. Because Malayalam has various basic compound characters which are more than 50. Also, generalization is failed with handwriting variations for different individuals. In some cases, frequent misclassification is occurred due to the similar characters. Moreover, larger training dataset has required higher computing power so, the efficiency was degrading. Therefore, to develop an intelligent framework which is used to extracts the Malayalam text from the images. To improves the pre-processing stage by adding the entire steps such as normalization, binarization, augmentation and noise removal. To overcome the Malayalam script complexity such as visually same characters, diacritical marks, etc. This paper develops a novel Bayesian Neural Network based Hybrid Cuckoo Search with Lyrebird (BNN-HCSL) algorithm to enhance the character recognition efficiency. Moreover, the developed BNN-HCSL replica was implemented on MATLAB platform and the comparative analysis was performed in terms of performance matrices such as accuracy, precision, recall, F-Measure and Word Error rate (WER) to analyse the effectiveness of the prediction. Moreover, the attained outcomes are demonstrates the better for proposed model in attaining accurate and scalable Malayalam handwritten image to text recognition. Also, this study has contributes the document digitization by accessing the Malayalam handwritten contents. text recognition Machine Learning Deep Learning generalization 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. 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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-9079840","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":612470801,"identity":"cc3bfc83-e04a-4453-a9b4-f05012702ade","order_by":0,"name":"Gargy G","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYBADOfvjzQeAtIQM0VqMGc4cSwBp4SFaSyLDDR8DEIOwFnn3s49ffGyrTWCcwfP51Y0aCx4G9sNHN+DTYngm3cxyZtvxPGbp3m3WOceADuNJS7uBV0tDGpsxb9uxYjaZs9uMc9iAWiR4zPBr6X/GZvy37Vhij0TOM+Ocf0RokZdIY37M2FaTOEMih/lxbhsRWgwknrEx9pw7YGzAc8yMObdPgoeNkF/k+9OYP/woq5MzYG9+/DnnW50cP/vhY/htOcDAJsHIdhjEZpMAk/iUg21pYGD+wPCnDsQGMkbBKBgFo2AUYAEAW/lLHT/3cOAAAAAASUVORK5CYII=","orcid":"","institution":"Noorul Islam University","correspondingAuthor":true,"prefix":"","firstName":"Gargy","middleName":"","lastName":"G","suffix":""},{"id":612470802,"identity":"519f5284-47eb-4483-bd14-63000eb4e366","order_by":1,"name":"A Shajin Nargunam","email":"","orcid":"","institution":"Noorul Islam University","correspondingAuthor":false,"prefix":"","firstName":"A","middleName":"Shajin","lastName":"Nargunam","suffix":""}],"badges":[],"createdAt":"2026-03-10 06:23:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9079840/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9079840/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108182619,"identity":"39dbfaca-a8b5-43d9-8e8b-175cbc35f5b2","added_by":"auto","created_at":"2026-04-30 08:59:27","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1420203,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9079840/v1_covered_396d6341-d899-4d31-9043-cbd140f8c303.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An Intelligent Handwritten Malayalam Text Recognition Using Optimized Machine Learning Based Optimization Frameworks","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":"text recognition, Machine Learning, Deep Learning, generalization","lastPublishedDoi":"10.21203/rs.3.rs-9079840/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9079840/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTraditional Machine Learning (ML) and Deep Learning (DL) models are failed to predict the characters, overlapping sentences and diacritical marks. 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