A graph-based approach for transcribing ancient documents

dc.contributor.authorMeza Lovón, Graciela Lecireth
dc.date.accessioned2019-01-29T22:19:55Z
dc.date.available2019-01-29T22:19:55Z
dc.date.issued2012
dc.description.abstractOver the last years, the interest in preserving digitally ancient documents has increased resulting in databases with a huge amount of image data. Most of these documents are not transcribed and thus querying operations are limited to basic searching. We propose a novel approach for transcribing historical documents and present results of our initial experiments. Our method divides a text-line image into frames and constructs a graph using the framed image. Then Dijkstra algorithm is applied to find the line transcription. Experiments show a character accuracy of 79.3%. © Springer-Verlag Berlin Heidelberg 2012.es_PE
dc.description.uriTrabajo de investigaciónes_PE
dc.identifier.doihttps://doi.org/10.1007/978-3-642-34654-5_22es_PE
dc.identifier.isbnurn:isbn:9783642346538es_PE
dc.identifier.issn3029743es_PE
dc.identifier.urihttps://hdl.handle.net/20.500.12590/15889
dc.language.isoenges_PE
dc.publisherSpringer Verlages_PE
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84906665587&doi=10.1007%2f978-3-642-34654-5_22&partnerID=40&md5=b77dd42f2a22019e94bffbafc7f2c212es_PE
dc.rightsinfo:eu-repo/semantics/restrictedAccesses_PE
dc.sourceRepositorio Institucional - UCSPes_PE
dc.sourceUniversidad Católica San Pabloes_PE
dc.sourceScopuses_PE
dc.subjectArtificial intelligencees_PE
dc.subjectExperimentses_PE
dc.subjectGraph theoryes_PE
dc.subjectSupport vector machineses_PE
dc.subjectAncient documentses_PE
dc.subjectDijkstra algorithmses_PE
dc.subjectGraph-basedes_PE
dc.subjectHandwriting recognitiones_PE
dc.subjectHistorical documentses_PE
dc.subjectImage dataes_PE
dc.subjectShortest path algorithmses_PE
dc.subjectTranscriptiones_PE
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#1.02.00es_PE
dc.titleA graph-based approach for transcribing ancient documentses_PE
dc.typeinfo:eu-repo/semantics/article
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