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Saliency-Based Detection of Identy Documents Captured by Smartphones

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Abstract

Smartphones have became an easy and convenient mean to acquire documents. In this paper, we focus on the automatic segmentation of identity documents in smartphone photos or videos using visual saliency (VS). VS-based approaches, which pertain to computer vision, have not be considered yet for this particular task. Here we compare different VS methods, and we propose a new VS schemebased on a recent distance belonging to the scope of mathematical morphology. We show that the saliency maps we obtain are competitive with state-of-the-art visual saliency methods and, that such approaches are very promising for use in identity document detection and segmentation, even without taking into account any prior knowledge about document contents. In particular they can work in real-time on smartphones.

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Bibtex (lrde.bib)

@InProceedings{	  movn.18.das,
  author	= {Minh {\^On V\~{u} Ng\d{o}c} and Jonathan Fabrizio and
		  Thierry G\'eraud},
  title		= {Saliency-Based Detection of Identy Documents Captured by
		  Smartphones},
  booktitle	= {Proceedings of the IAPR International Workshop on Document
		  Analysis Systems (DAS)},
  year		= {2018},
  month		= {April},
  address	= {Vienna, Austria},
  abstract	= {Smartphones have became an easy and convenient mean to
		  acquire documents. In this paper, we focus on the automatic
		  segmentation of identity documents in smartphone photos or
		  videos using visual saliency (VS). VS-based approaches,
		  which pertain to computer vision, have not be considered
		  yet for this particular task. Here we compare different VS
		  methods, and we propose a new VS scheme, based on a recent
		  distance belonging to the scope of mathematical morphology.
		  We show that the saliency maps we obtain are competitive
		  with state-of-the-art visual saliency methods and, that
		  such approaches are very promising for use in identity
		  document detection and segmentation, even without taking
		  into account any prior knowledge about document contents.
		  In particular they can work in real-time on smartphones.}
}