Enhanced Traceability Methodology Based on Ocr Deep Learning Techniques in the Metalworking Industry

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UDC.coleccionInvestigación
UDC.departamentoEnxeñaría Industrial
UDC.departamentoEnxeñaría Naval e Industrial
UDC.grupoInvCiencia e Técnica Cibernética (CTC)
UDC.grupoInvSistemas Térmicos e Transferencia de Calor (SISTER)
UDC.issue3
UDC.journalTitleLogic Journal of the IGPL
UDC.startPagejzaf080
UDC.volume34
dc.contributor.authorArcano-Bea, Paula
dc.contributor.authorGarcía-Fischer, Agustín
dc.contributor.authorFariñas Alvariño, Pablo
dc.contributor.authorZayas-Gato, Francisco
dc.contributor.authorQuintián, Héctor
dc.date.accessioned2026-05-04T09:25:53Z
dc.date.available2026-05-04T09:25:53Z
dc.date.issued2026-04-27
dc.descriptionFinanciado para publicación en acceso aberto: Universidade da Coruña/CISUG
dc.description.abstract[Abstract] The implementation of digital traceability solutions has become essential to improve the efficiency and ensure the quality of the production processes across all industries. In the shipbuilding sector, where subcontractors play a vital role, the ability to effectively track and document each part produced is extremely important. This study focuses on the implementation of a traceability system in a Spanish metal manufacturing company supplying a shipyard by evaluating various image processing techniques and multiple Optical Character Recognition to identify marked parts and determine the most effective solution for this application. The results of the implementation of these models were successful and represent a viable solution for the improvement of the efficiency and accessibility of information retrieval processes within the industry.
dc.description.sponsorshipPaula Arcano-Bea's research was supported by the Xunta de Galicia (Regional Government of Galicia) through grants to Ph.D. (http://gain.xunta.gal), under the "Axudas á etapa predoutoral" grant with reference: ED481A-2025-088. This research is the result of the Strategic Project “Critical infrastructures cybersecure through intelligent modeling of attacks, vulnerabilities and increased security of their IoT devices for the water supply sector” (C061/23), as a result of the collaboration agreement signed between the National Institute of Cybersecurity (INCIBE) and the University of A Coruña. This initiative is carried out within the framework of the funds of the Recovery Plan, Transformation and Resilience Plan funds, financed by the European Union (Next Generation). This work has been supported by Xunta de Galicia through Axencia Galega de Innovación (GAIN) by grant IN853C 2022/01, Centro Mixto de Investigación UDC-NAVANTIA ‘O estaleiro do futuro’, which is ongoing until the end of September 2025. The support was inherited from both the starting and consolidation stages of the same project throughout 2015-2018 and 2018-2021, respectively. This stage is also co-funded by ERDF funds from the EU in the framework of program FEDER Galicia 2021-2027. Xunta de Galicia. Grants for the consolidation and structuring of competitive research units, GPC (ED431B 2023/49). CITIC, as a center accredited for excellence within the Galician University System and a member of the CIGUS Network, receives subsidies from the Department of Education, Science, Universities, and Vocational Training of the Xunta de Galicia. Additionally, it is co-financed by the EU through the FEDER Galicia 2021-27 operational program (Ref. ED431G 2023/01)
dc.description.sponsorshipXunta de Galicia; ED481A-2025-088
dc.description.sponsorshipXunta de Galicia; ED431B 2023/49
dc.description.sponsorshipXunta de Galicia; ED431G 2023/01
dc.description.sponsorshipXunta de Galicia; IN853C 2022/01
dc.description.sponsorshipInstituto Nacional de Ciberseguridad; C061/23
dc.identifier.citationPaula Arcano-Bea, Agustín García-Fischer, Pablo Fariñas, Francisco Zayas-Gato, Héctor Quintián, Enhanced traceability methodology based on OCR deep learning techniques in the metalworking industry, Logic Journal of the IGPL, Volume 34, Issue 3, June 2026, jzaf080, https://doi.org/10.1093/jigpal/jzaf080
dc.identifier.doi10.1093/jigpal/jzaf080
dc.identifier.issn1368-9894
dc.identifier.urihttps://hdl.handle.net/2183/48152
dc.language.isoeng
dc.publisherOxford University Press
dc.relation.urihttps://doi.org/10.1093/jigpal/jzaf080
dc.rightsAttribution 4.0 Internationalen
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectOptical character recognition
dc.subjectTesseract
dc.subjectEasyOCR
dc.subjectTR-OCR
dc.subjectMMOCR
dc.subjectKerasOCR
dc.subjectPaddleOCR
dc.subjectTraceability
dc.subjectMetal manufacturing
dc.titleEnhanced Traceability Methodology Based on Ocr Deep Learning Techniques in the Metalworking Industry
dc.typejournal article
dc.type.hasVersionVoR
dspace.entity.typePublication
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