Planting, Growing and Pruning Trees: Connected Filters Applied to Document Image Analysis

Authors

Guillaume Lazzara, Thierry Géraud, Roland Levillain

Where

Proceedings of the 11th IAPR International Workshop on Document Analysis Systems (DAS)

Place

Tours, France

Type

inproceedings

Projects

Olena

Date

2013-12-10

Abstract

Mathematical morphology, when used in the field of document image analysis and processing, is often limited to some classical yet basic tools. The domain however features a lesser-known class of powerful operators, called connected filters. These operators present an important property: they do not shift nor create contours. Most connected filters are linked to a tree-based representation of an image's contents, where nodes represent connected components while edges express an inclusion relation. By computing attributes for each node of the tree from the corresponding connected component, then selecting nodes according to an attribute-based criterion, one can either filter or recognize objects in an image. This strategy is very intuitive, efficient, easy to implement, and actually well-suited to processing images of magazines. Examples of applications include image simplification, smart binarization, and object identification.

Bibtex (lrde.bib)

@InProceedings{	  lazzara.14.das,
  author	= {Guillaume Lazzara and Thierry G\'eraud and Roland
		  Levillain},
  title		= {Planting, Growing and Pruning Trees: Connected Filters
		  Applied to Document Image Analysis},
  booktitle	= {Proceedings of the 11th IAPR International Workshop on
		  Document Analysis Systems (DAS)},
  year		= 2014,
  address	= {Tours, France},
  month		= apr,
  organization	= {IAPR},
  note		= {Accepted},
  project	= {Image},
  abstract	= {Mathematical morphology, when used in the field of
		  document image analysis and processing, is often limited to
		  some classical yet basic tools. The domain however features
		  a lesser-known class of powerful operators, called
		  connected filters. These operators present an important
		  property: they do not shift nor create contours. Most
		  connected filters are linked to a tree-based representation
		  of an image's contents, where nodes represent connected
		  components while edges express an inclusion relation. By
		  computing attributes for each node of the tree from the
		  corresponding connected component, then selecting nodes
		  according to an attribute-based criterion, one can either
		  filter or recognize objects in an image. This strategy is
		  very intuitive, efficient, easy to implement, and actually
		  well-suited to processing images of magazines. Examples of
		  applications include image simplification, smart
		  binarization, and object identification. }
}