dc.contributorRamírez-Ortegón, M.A., Institut für Informatik, Freie Universität Berlin, Takustr. 9, 14195 Berlin, Germany; Tapia, E., Institut für Informatik, Freie Universität Berlin, Takustr. 9, 14195 Berlin, Germany; Rojas, R., Institut für Informatik, Freie Universität Berlin, Takustr. 9, 14195 Berlin, Germany; Cuevas, E., Department of Computer Science, University of Guadalajara, Av. Revolución 1500, Guadalajara, Jalisco, Mexico
dc.creatorRamirez-Ortegon, M.A.
dc.creatorTapia, E.
dc.creatorRojas, R.
dc.creatorCuevas, E.
dc.date.accessioned2015-11-19T18:55:24Z
dc.date.accessioned2022-11-02T15:45:25Z
dc.date.available2015-11-19T18:55:24Z
dc.date.available2022-11-02T15:45:25Z
dc.date.created2015-11-19T18:55:24Z
dc.date.issued2010
dc.identifierhttp://hdl.handle.net/20.500.12104/68360
dc.identifier10.1016/j.patcog.2010.04.028
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-77953609409&partnerID=40&md5=8252d0da0e377a3355eecd625421018b
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/5018067
dc.description.abstractThe transition method for image binarization is based on the concept of t-transition pixels, a generalization of edge pixels, and t-transition sets. We introduce a novel unsupervised thresholding for unimodal histograms to estimate the transition sets. We also present dilation and incidence transition operators to refine the transition set. Afterward, we propose the simple edge transition operator for detecting edges. Our experiments show that the new approach increases the effectiveness of OCR applications outperforming several top-ranked binarization algorithms. © 2010 Elsevier Ltd. All rights reserved.
dc.relationPattern Recognition
dc.relation43
dc.relation10
dc.relation3243
dc.relation3254
dc.relationScopus
dc.relationWOS
dc.titleTransition thresholds and transition operators for binarization and edge detection
dc.typeArticle


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