Automatic Detection and Recognition of Text-Based Traffic Signs from Images

dc.contributorUniv Oeste Paulista
dc.contributorUniversidade Estadual Paulista (Unesp)
dc.contributorUniv Fortaleza
dc.date.accessioned2019-10-04T12:15:40Z
dc.date.accessioned2022-12-19T17:57:44Z
dc.date.available2019-10-04T12:15:40Z
dc.date.available2022-12-19T17:57:44Z
dc.date.created2019-10-04T12:15:40Z
dc.date.issued2018-12-01
dc.identifierIeee Latin America Transactions. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 16, n. 12, p. 2947-2953, 2018.
dc.identifier1548-0992
dc.identifierhttp://hdl.handle.net/11449/184663
dc.identifier10.1109/TLA.2018.8804261
dc.identifierWOS:000482564600015
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/5365717
dc.description.abstractDetection and recognition of texts in traffic signs has been widely studied and with the advance in image capture technology has helped to improve or to create new methods to achieve this issue. In this work, we presented a method for detection, segmentation and recognition of text-based traffic signs from images analyzing and processing techniques. The results show that the computational cost and accuracy rate considering the proposed approach are acceptable to real time applications, with an execution time under 0.5 seconds, with a hit rate of 94.38% in the plate detection, 83.42% in the character segmentation and 89.23 in the digit classification.
dc.languageeng
dc.publisherIeee-inst Electrical Electronics Engineers Inc
dc.relationIeee Latin America Transactions
dc.rightsAcesso aberto
dc.sourceWeb of Science
dc.subjectTraffic signs detection
dc.subjectTraffic signs recognition
dc.subjectCharacters segmentation
dc.subjectOCR
dc.titleAutomatic Detection and Recognition of Text-Based Traffic Signs from Images
dc.typeArtículos de revistas


Este ítem pertenece a la siguiente institución