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Advancing democratic processes in Ecuador: A case study on neural network-driven OCR for election report verification

dc.contributor.authorAsimbaya J.A.M
dc.contributor.authorRamírez G.M
dc.contributor.authorDíaz-Arancibia J.
dc.date.accessioned2024-07-31T21:06:48Z
dc.date.available2024-07-31T21:06:48Z
dc.date.created2024
dc.identifier.issn2664720
dc.identifier.urihttp://hdl.handle.net/11407/8397
dc.descriptionBackground: In Ecuador, scepticism surrounding electoral outcomes underscores the need for a reliable system to ensure transparent election results. Manual verification demands a more efficient approach due to the vast volume of election reports. This research introduces an automated system leveraging Artificial Intelligence to process results from Ecuador's three recent national elections. Methods: The system, designed with a three-layer architecture, extracts, processes, analyses, classifies and compares election results. We thoroughly analysed the National Electoral Council of Ecuador (CNE) web pages for effective data extraction and processing. Rigorous unit and acceptance tests validated the system's functionality. A classifier model, trained using data augmentation techniques, achieved a 98% accuracy rate. Results: While the system boasts high efficiency, we identified three errors, accounting for less than 5% of the total fields processed. Notably, the quality of scanned reports and illegible handwritten numbers posed challenges for the classifier. Conclusions: The system's deployment by an authorized entity in Ecuador could enhance the CNE's information verification. Despite some errors, the system's potential is clear. Future work includes refining classifiers, verifying officer signatures, and expanding the system's scope, aiming for a more transparent electoral process. © 2024 John Wiley & Sons Ltd.
dc.language.isoeng
dc.publisherJohn Wiley and Sons Inc
dc.relation.isversionofhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85188688207&doi=10.1111%2fexsy.13578&partnerID=40&md5=eed98929992247e638992bda2cafcffc
dc.sourceExpert Systems
dc.sourceExpert Syst
dc.sourceScopus
dc.subjectartificial intelligence in electionseng
dc.subjectautomated election report processingeng
dc.subjectdigital election transparencyeng
dc.subjectelectoral data verificationeng
dc.subjectfraud detectioneng
dc.subjectArtificial intelligenceeng
dc.subjectAutomationeng
dc.subjectData handlingeng
dc.subjectOptical character recognitioneng
dc.subjectWebsiteseng
dc.subjectArtificial intelligence in electioneng
dc.subjectAutomated election report processingeng
dc.subjectCase-studieseng
dc.subjectData verificationeng
dc.subjectDemocratic processeng
dc.subjectDigital election transparencyeng
dc.subjectEcuadoreng
dc.subjectElectoral dataeng
dc.subjectElectoral data verificationeng
dc.subjectFraud detectioneng
dc.subjectAcceptance testseng
dc.titleAdvancing democratic processes in Ecuador: A case study on neural network-driven OCR for election report verificationeng
dc.typearticle
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.publisher.programIngeniería de Sistemasspa
dc.type.spaArtículo
dc.identifier.doi10.1111/exsy.13578
dc.publisher.facultyFacultad de Ingenieríasspa
dc.affiliationAsimbaya, J.A.M., Master of Artificial Intelligence, Universidad Internacional de La Rioja, La Rioja, Spain
dc.affiliationRamírez, G.M., Facultad de Ingenierías, Universidad de Medellín, Medellín, Colombia
dc.affiliationDíaz-Arancibia, J., Departamento de Ciencias de la Computación e Informática, Universidad de La Frontera, Temuco, Chile
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dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.identifier.reponamereponame:Repositorio Institucional Universidad de Medellín
dc.identifier.repourlrepourl:https://repository.udem.edu.co/
dc.identifier.instnameinstname:Universidad de Medellín


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