TextCatcher: A method to detect curved and challenging text in natural scenes

Authors

Jonathan Fabrizio, Myriam Robert-Seidowsky, Séverine Dubuisson, Stefania Calarasanu, Raphaël Boissel

Journal

International Journal on Document Analysis and Recognition

Type

article

Publisher

Springer

Projects

Olena

Keywords

Image

Date

2016-04-08

Abstract

In this paper, we propose a text detection algorithm which is hybrid and multi-scale. First, it relies on a connected component-based approach: After the segmentation of the image, a classification step using a new wavelet descriptor spots the letters. A new graph modeling and its traversal procedure allow to form candidate text areas. Second, a texture-based approach discards the false positives. Finally, the detected text areas are precisely cut out and a new binarization step is introduced. The main advantage of our method is that few assumptions are put forward. Thus, “challenging texts” like multi-sizedmulti-colored, multi-oriented or curved text can be localized. The efficiency of TextCatcher has been validated on three different datasets: Two come from the ICDAR competition, and the third one contains photographs we have taken with various daily life texts. We present both qualitative and quantitative results.

Documents

• Paper

Bibtex (lrde.bib)

@Article{	  fabrizio.16.ijdar,
  author	= {Jonathan Fabrizio and Myriam Robert-Seidowsky and
		  S\'everine Dubuisson and Stefania Calarasanu and Rapha\"el
		  Boissel},
  title		= {TextCatcher: {A} method to detect curved and challenging
		  text in natural scenes},
  journal	= {International Journal on Document Analysis and
		  Recognition},
  year		= 2016,
  volume	= 19,
  number	= 2,
  publisher	= {Springer},
  pages		= {99--117},
  month		= feb,
  abstract	= {In this paper, we propose a text detection algorithm which
		  is hybrid and multi-scale. First, it relies on a connected
		  component-based approach: After the segmentation of the
		  image, a classification step using a new wavelet descriptor
		  spots the letters. A new graph modeling and its traversal
		  procedure allow to form candidate text areas. Second, a
		  texture-based approach discards the false positives.
		  Finally, the detected text areas are precisely cut out and
		  a new binarization step is introduced. The main advantage
		  of our method is that few assumptions are put forward.
		  Thus, ``challenging texts'' like multi-sized,
		  multi-colored, multi-oriented or curved text can be
		  localized. The efficiency of TextCatcher has been validated
		  on three different datasets: Two come from the ICDAR
		  competition, and the third one contains photographs we have
		  taken with various daily life texts. We present both
		  qualitative and quantitative results.},
  doi		= {10.1007/s10032-016-0264-4}
}