Título:
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Visual semantic re-ranker for text spotting
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Autor/a:
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Sabir, Ahmed; Moreno-Noguer, Francesc; Padró, Lluís
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Otros autores:
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Institut de Robòtica i Informàtica Industrial; Universitat Politècnica de Catalunya. Departament de Ciències de la Computació; Universitat Politècnica de Catalunya. ROBiri - Grup de Robòtica de l'IRI; Universitat Politècnica de Catalunya. GPLN - Grup de Processament del Llenguatge Natural |
Abstract:
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The final publication is available at link.springer.com |
Abstract:
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Many current state-of-the-art methods for text recognition are based on purely local information and ignore the semantic corre- lation between text and its surrounding visual context. In this paper, we propose a post-processing approach to improve the accuracy of text spotting by using the semantic relation between the text and the scene. We initially rely on an off-the-shelf deep neural network that provides a series of text hypotheses for each input image. These text hypotheses are then re-ranked using the semantic relatedness with the object in the image. As a result of this combination, the performance of the original network is boosted with a very low computational cost. The proposed framework can be used as a drop-in complement for any text-spotting algorithm that outputs a ranking of word hypotheses. We validate our approach on ICDAR’17 shared task dataset. |
Abstract:
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Peer Reviewed |
Materia(s):
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-Àrees temàtiques de la UPC::Informàtica::Automàtica i control -Computer vision -Text spotting -Deep learning -Semantic visual context -Classificació INSPEC::Pattern recognition::Computer vision |
Derechos:
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Tipo de documento:
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Artículo - Versión presentada Objeto de conferencia |
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