Difference between revisions of "Publications/fabrizio.16.ijdar"
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| title = TextCatcher: a method to detect curved and challenging text in natural scenes |
| title = TextCatcher: a method to detect curved and challenging text in natural scenes |
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| journal = International Journal on Document Analysis and Recognition |
| journal = International Journal on Document Analysis and Recognition |
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| − | | volume = |
+ | | volume = 19 |
| − | | number = |
+ | | number = 2 |
| publisher = Springer |
| publisher = Springer |
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| − | | pages = |
+ | | pages = 99 to 117 |
| 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. |
| 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. |
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| lrdeprojects = Image |
| lrdeprojects = Image |
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Recognition<nowiki>}</nowiki>, |
Recognition<nowiki>}</nowiki>, |
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year = 2016, |
year = 2016, |
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| − | volume = |
+ | volume = 19, |
| − | number = |
+ | number = 2, |
publisher = <nowiki>{</nowiki>Springer<nowiki>}</nowiki>, |
publisher = <nowiki>{</nowiki>Springer<nowiki>}</nowiki>, |
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| − | pages = <nowiki>{</nowiki> |
+ | pages = <nowiki>{</nowiki>99--117<nowiki>}</nowiki>, |
month = feb, |
month = feb, |
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abstract = <nowiki>{</nowiki>In this paper, we propose a text detection algorithm which |
abstract = <nowiki>{</nowiki>In this paper, we propose a text detection algorithm which |
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Revision as of 10:34, 17 May 2016
- 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
- Image"Image" is not in the list (Vaucanson, Spot, URBI, Olena, APMC, Tiger, Climb, Speaker ID, Transformers, Bison, ...) of allowed values for the "Related project" property.
- 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.
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.}
}