We are happy to announce that the following paper has been accepted
for publication in the International Journal of Document Analysis and
Recognition (IJDAR):

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

Jonathan Fabrizio (1) Myriam Robert-Seidowsky (1) Séverine Dubuisson (2) 
Stefania Calarasanu (1) Raphaël Boissel (1)

(1) EPITA Research and Development Laboratory (LRDE)
(2) Sorbonne Universités, UPMC - ISIR

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.