Invoices Classification Using Deep Features Based on SME Perspectives

Invoices Classification Using Deep Features Based on SME Perspectives | 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 Invoices Classification Using Deep Features Based on SME Perspectives Nurul Akmar Azman, Azlinah Mohamed, Amsyar Mohmad Jamil This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1041467/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 Automation is seen as a potential alternative in improving productivity in the twenty-first century. Invoicing is the essential foundation of accounting record keeping and serves as a critical foundation for law enforcement inspections by auditing agencies and tax authorities. With the rise of artificial intelligence, automated record keeping systems are becoming more widespread in major organizations, allowing them to do tasks in real time and with no effort as well as a decision-making tool. Despite the system's benefits, many small and medium-sized businesses, particularly in Malaysia, are hesitant to implement it. Invoices are mostly processed manually that prone to human errors and lower productivity of the company. Artificial intelligence will further improve automated invoice handling making it simpler and efficient for all levels of businesses especially the small and medium enterprise This study presents a deep learning approach on record keeping focusing on invoices recognition by detecting invoice image classification. The deep learning model used in this research including the classic architecture of Convolutional Neural Network and its other variation such as VGG-16, VGG-19 and ResNet-50. Besides that, the constrains and expectation of the system to be implemented in small and medium enterprise in Malaysia are also presented in the interview scores. The research highlighted a comparison result between deep learning model and the perspective of SME presented in the discussion section. ResNet-50 shows a significant value in both training and validation accuracy compared to the other models with 95.90% accuracy in training and 74.24% accuracy for validation data. Future work will look at the suggested other deep learning method and intelligence features to be implemented for a more efficient invoices recognition and for small and medium enterprise. Geographic Information Systems Electrical Engineering Invoice Classification SME Deep Learning Deep Features Machine Learning Full Text 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-1041467","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research","associatedPublications":[],"authors":[{"id":61769471,"identity":"242326d2-f986-494d-abe3-ee448b429295","order_by":0,"name":"Nurul Akmar Azman","email":"","orcid":"","institution":"Universiti Teknologi MARA","correspondingAuthor":false,"prefix":"","firstName":"Nurul","middleName":"Akmar","lastName":"Azman","suffix":""},{"id":61769472,"identity":"44fb2a6b-aab0-4828-aa44-7e29b6dff5d4","order_by":1,"name":"Azlinah Mohamed","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIie3QvQqCUBTA8SMXrMFyi0DSV1CEFl9GCXIpcGxwuC2OudZb+AjKIVsEV4eGJucgaJRuOgXhxxZx/3AOnOG3HAAe7ycjNAaIVSDN1YcINTGBkAGErdih0JfIYbJHz7+60UU+R7CzHDrL9FYyjxOKx7TcRkjEAjLXocqmnUCypyiJ2BAhwG6iocBIha5ek6oH0VNGJgHaDaE9iJG9yQGNE5JlYaeuGShrr5WoOeJDeqI2zZOyuPvWIlRWUSv5zGYjwrjjY18a3QYTHo/H++teks9N8ffNnI4AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-4582-8763","institution":"Universiti Teknologi MARA","correspondingAuthor":true,"prefix":"","firstName":"Azlinah","middleName":"","lastName":"Mohamed","suffix":""},{"id":61769473,"identity":"010e82a4-5beb-4a16-b2b4-9a5f9dcc905f","order_by":2,"name":"Amsyar Mohmad Jamil","email":"","orcid":"","institution":"MVS Absolute Ent.","correspondingAuthor":false,"prefix":"","firstName":"Amsyar","middleName":"Mohmad","lastName":"Jamil","suffix":""}],"badges":[],"createdAt":"2021-11-02 01:55:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1041467/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1041467/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":28247523,"identity":"42090f32-2867-405a-9740-644f93114085","added_by":"auto","created_at":"2022-10-25 20:21:55","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":364931,"visible":true,"origin":"","legend":"","description":"","filename":"EURASIP2021azlinah.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1041467/v1_covered.pdf"},{"id":15367393,"identity":"e9c468c6-6cac-4098-bc86-37920b27b13b","added_by":"auto","created_at":"2021-11-09 16:22:53","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":365043,"visible":true,"origin":"","legend":"","description":"","filename":"EURASIP2021azlinah.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1041467/v1_covered.pdf"}],"financialInterests":"","formattedTitle":"Invoices Classification Using Deep Features Based on SME Perspectives","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-1041467/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"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":false,"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":"Invoice Classification, SME, Deep Learning, Deep Features, Machine Learning","lastPublishedDoi":"10.21203/rs.3.rs-1041467/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1041467/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Automation is seen as a potential alternative in improving productivity in the twenty-first century. Invoicing is the essential foundation of accounting record keeping and serves as a critical foundation for law enforcement inspections by auditing agencies and tax authorities. With the rise of artificial intelligence, automated record keeping systems are becoming more widespread in major organizations, allowing them to do tasks in real time and with no effort as well as a decision-making tool. Despite the system's benefits, many small and medium-sized businesses, particularly in Malaysia, are hesitant to implement it. Invoices are mostly processed manually that prone to human errors and lower productivity of the company. Artificial intelligence will further improve automated invoice handling making it simpler and efficient for all levels of businesses especially the small and medium enterprise This study presents a deep learning approach on record keeping focusing on invoices recognition by detecting invoice image classification. The deep learning model used in this research including the classic architecture of Convolutional Neural Network and its other variation such as VGG-16, VGG-19 and ResNet-50. Besides that, the constrains and expectation of the system to be implemented in small and medium enterprise in Malaysia are also presented in the interview scores. The research highlighted a comparison result between deep learning model and the perspective of SME presented in the discussion section. ResNet-50 shows a significant value in both training and validation accuracy compared to the other models with 95.90% accuracy in training and 74.24% accuracy for validation data. 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